{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:528: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:529: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:530: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
      "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:535: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
     ]
    }
   ],
   "source": [
    "from PIL import Image\n",
    "import os\n",
    "\n",
    "import matplotlib\n",
    "\n",
    "import math\n",
    "import h5py\n",
    "\n",
    "import cv2\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import glob\n",
    "import scipy.misc\n",
    "\n",
    "from sklearn.metrics import jaccard_score \n",
    "import tensorflow as tf\n",
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting paths for logits, ground truth and post processed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#result_path = \"/home/pf/pfshare/data/MA_Rajanie/Post_processing_filter/Result/processed_raw\"\n",
    "logits_path= \"/home/pf/pfshare/data/MA_Rajanie/models/research/deeplab/datasets/lake/exp/PTZ_and_nonPTZ_cam0/Test/Moritz_cam1_16-17/logits\"\n",
    "labels_path = \"/home/pf/pfshare/data/MA_Rajanie/Post_processing_filter/Result/labels_raw_cam0\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating numpy arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "files = (glob.glob(logits_path + \"/*\"))\n",
    "files = (sorted(files))\n",
    "logits=np.array([np.array(np.load(file)) for file in files])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1180, 393250, 5)\n",
      "(1178, 393250, 5)\n"
     ]
    }
   ],
   "source": [
    "print(logits.shape)\n",
    "logits = logits[:-2]\n",
    "print(logits.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = (glob.glob(labels_path+ \"/*\"))\n",
    "labels = (sorted(labels))\n",
    "labels = np.array([np.array((Image.open(fname)).resize((1210,325))) for fname in labels])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels=labels.reshape((labels.shape[0],-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1178, 393250)\n",
      "(1178, 393250, 5)\n"
     ]
    }
   ],
   "source": [
    "print(labels.shape)\n",
    "print(logits.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# n = 200  # for 2 random indices\n",
    "# index = np.random.choice(labels.shape[0], n, replace=False)  \n",
    "\n",
    "# x_random = logits[index]\n",
    "# y_random = labels[index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_1=logits.reshape((-1,5))\n",
    "labels_1=labels.reshape((-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(463248500, 5)\n",
      "(463248500,)\n",
      "(463248500,)\n"
     ]
    }
   ],
   "source": [
    "print(logits_1.shape)\n",
    "print(labels_1.shape)\n",
    "preds=np.argmax(logits_1,axis=1)\n",
    "print(preds.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(463248500, 5)\n",
      "(463248500, 5)\n"
     ]
    }
   ],
   "source": [
    "labels_onehot = np.eye(5)[labels_1]\n",
    "print(labels_onehot.shape)\n",
    "logits_onehot = np.eye(5)[preds]\n",
    "print(logits_onehot.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Precision Recall Curve for each class "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"labels.npy\", labels_onehot)\n",
    "np.save(\"logits.npy\", logits_onehot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2316242612\n",
      "[6.47600586e-09 0.00000000e+00 1.00000000e+00] [1. 0. 0.]\n",
      "6.476005858626633e-09\n",
      "2316242612\n",
      "[0.47380683 0.9989249  1.        ] [1.         0.76199703 0.        ]\n",
      "0.8739452395048198\n",
      "2316242612\n",
      "[0.08757537 0.4184508  1.        ] [1.         0.99398928 0.        ]\n",
      "0.4164619996925848\n",
      "2316242612\n",
      "[0.35649314 0.9497547  1.        ] [1.        0.8732057 0.       ]\n",
      "0.8745325108754174\n",
      "2316242612\n",
      "[0.08212465 0.69414091 1.        ] [1.         0.82761029 0.        ]\n",
      "0.5886356042861898\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-32dc4ce2dab3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0mlogits\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"logits.npy\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(labels.ravel(),\n\u001b[0;32m---> 30\u001b[0;31m     logits.ravel())\n\u001b[0m\u001b[1;32m     31\u001b[0m average_precision[\"micro\"] = average_precision_score(labels, logits,\n\u001b[1;32m     32\u001b[0m                                                      average=\"micro\")\n",
      "\u001b[0;32m~/Documents/Thesis/venv/lib/python3.7/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36mprecision_recall_curve\u001b[0;34m(y_true, probas_pred, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    522\u001b[0m     fps, tps, thresholds = _binary_clf_curve(y_true, probas_pred,\n\u001b[1;32m    523\u001b[0m                                              \u001b[0mpos_label\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpos_label\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 524\u001b[0;31m                                              sample_weight=sample_weight)\n\u001b[0m\u001b[1;32m    525\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    526\u001b[0m     \u001b[0mprecision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtps\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtps\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Thesis/venv/lib/python3.7/site-packages/sklearn/metrics/ranking.py\u001b[0m in \u001b[0;36m_binary_clf_curve\u001b[0;34m(y_true, y_score, pos_label, sample_weight)\u001b[0m\n\u001b[1;32m    406\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    407\u001b[0m     \u001b[0;31m# ensure binary classification if pos_label is not specified\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 408\u001b[0;31m     \u001b[0mclasses\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_true\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    409\u001b[0m     if (pos_label is None and\n\u001b[1;32m    410\u001b[0m         not (np.array_equal(classes, [0, 1]) or\n",
      "\u001b[0;32m<__array_function__ internals>\u001b[0m in \u001b[0;36munique\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[0;32m~/Documents/Thesis/venv/lib/python3.7/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(ar, return_index, return_inverse, return_counts, axis)\u001b[0m\n\u001b[1;32m    260\u001b[0m     \u001b[0mar\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masanyarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    261\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 262\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_unique1d\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_index\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_inverse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreturn_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    263\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_unpack_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    264\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/Thesis/venv/lib/python3.7/site-packages/numpy/lib/arraysetops.py\u001b[0m in \u001b[0;36m_unique1d\u001b[0;34m(ar, return_index, return_inverse, return_counts)\u001b[0m\n\u001b[1;32m    312\u001b[0m     \u001b[0mmask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbool_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    313\u001b[0m     \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 314\u001b[0;31m     \u001b[0mmask\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    315\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    316\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0maux\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "from sklearn.metrics import average_precision_score\n",
    "from sys import getsizeof\n",
    "\n",
    "n_classes=5\n",
    "# For each class\n",
    "precision = dict()\n",
    "recall = dict()\n",
    "average_precision = dict()\n",
    "for i in range(n_classes):\n",
    "    labels = np.load(\"labels.npy\").astype(np.uint8)\n",
    "    labels_onehot = labels[:, i]\n",
    "    print(getsizeof(labels))\n",
    "    del labels\n",
    "    logits = np.load(\"logits.npy\").astype(np.uint8)\n",
    "    logits_onehot = logits[:, i]\n",
    "    del logits\n",
    "    \n",
    "    precision[i], recall[i], _ = precision_recall_curve(labels_onehot,\n",
    "                                                        logits_onehot)\n",
    "    \n",
    "    print(precision[i], recall[i])\n",
    "    average_precision[i] = average_precision_score(labels_onehot, logits_onehot)\n",
    "    print(average_precision[i])\n",
    "\n",
    "# A \"micro-average\": quantifying score on all classes jointly\n",
    "\n",
    "# labels = np.load(\"labels.npy\")\n",
    "# logits = np.load(\"logits.npy\")\n",
    "# precision[\"micro\"], recall[\"micro\"], _ = precision_recall_curve(labels.ravel(),\n",
    "#     logits.ravel())\n",
    "# average_precision[\"micro\"] = average_precision_score(labels, logits,\n",
    "#                                                      average=\"micro\")\n",
    "# print('Average precision score, micro-averaged over all classes: {0:0.2f}'\n",
    "#       .format(average_precision[\"micro\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'micro'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-6374d515709b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;34m'step'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             else {})\n\u001b[0;32m----> 6\u001b[0;31m plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,\n\u001b[0m\u001b[1;32m      7\u001b[0m          where='post')\n\u001b[1;32m      8\u001b[0m plt.fill_between(recall[\"micro\"], precision[\"micro\"], alpha=0.2, color='b',\n",
      "\u001b[0;31mKeyError\u001b[0m: 'micro'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from inspect import signature\n",
    "plt.figure()\n",
    "step_kwargs=({'step':'post'}\n",
    "            if 'step' in signature(plt.fill_between).parameters\n",
    "            else {})\n",
    "plt.step(recall['micro'], precision['micro'], color='b', alpha=0.2,\n",
    "         where='post')\n",
    "plt.fill_between(recall[\"micro\"], precision[\"micro\"], alpha=0.2, color='b',\n",
    "                 **step_kwargs)\n",
    "\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.title(\n",
    "    'Average precision score, micro-averaged over all classes: AP={0:0.2f}'\n",
    "    .format(average_precision[\"micro\"]))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcEAAAITCAYAAAB/padRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nOydd3xb1fn/30fDtrzteMZkL0tQdgMhUEbZsxCzC5TRllJa2jJKKXvzA9ovdNBSoGxacIBQRstq2RASZrDsOHt5W/LSls7vjyspsmM7HhpX9nm/XnnFd+jec6Urfe55znOej5BSolAoFArFZMSQ6gYoFAqFQpEqlAgqFAqFYtKiRFChUCgUkxYlggqFQqGYtCgRVCgUCsWkRYmgQqFQKCYtSgQVKUEI8Y0Q4pCd7DNdCNErhDAmqVkJRwixQQhxePjvG4UQT6a6TenKzt4/IcTZQojXx3DcmUIIKYQwja+FinRAiaCiH+EfaXdYfFqEEI8KIXLjfR4p5a5Syv/tZJ9NUspcKWUw3ucP/4D6w9fpFEJ8KIRYFO/zTDSEEP8TQlyU6nYMZDDhklI+JaU8MpXtUugfJYKKwThBSpkL7A3sC1w7cAehke73zz/D11kC/Bd4LsXtiTuqN6NQDE+6/4gpEoiUcivwGrAbRHsBtwkhPgBcwGwhRIEQ4mEhRJMQYqsQ4tbY8KUQ4odCCLsQokcIUSeE2Du8PjYsuFAIsUII0R3uff4uvL7f070QYqoQ4iUhRKcQYo0Q4ocx57lRCPGsEOLx8Lm+EULsO8LrDABPAVVCiNKYYx4vhPgipqe4e8y2aUKI54UQbUKIDiHEH8Pr5wgh3g6vaxdCPCWEKBzL+y+EOCl8/m4hxFohxNED37uYa39ywHt2oRBiE/C2EOI1IcSlA479pRDilPDf1UKIN8Lva4MQ4rQh2nMbcBDwx3APOnLNBwghPhVCdIX/P2CYa9oghLhSCPGVEKIvfO+Uh9vYI4R4UwhRFN73ECHElkFef/ggh343/L8z3LZFQogfCCHeH6YtFiHEvUKIjeG2vy+EsAyy3/kx9/A6IcSPY7aVCCFeDt8jnUKI9yIPh0KIX4e/Ez3h9/W7Q7VFkTqUCCqGRAgxDTgW+Dxm9TnAj4A8YCPwKBAA5gJ7AUcCF4VffypwI3AukA+cCHQMcqr7gPuklPnAHODZIZr0D2ALMBWoAW4XQhwWs/3E8D6FwEvAH0d4nRnhNnYAjvC6vYBHgB8DU4C/Ai8JITLDIv9y+PpnAlXh8wII4I5wG63AtPB7MCqEEAuBx4Erw9fzHWDDKA5xcPj8RwHPAGfGHNsGzABeEULkAG8ATwNlwBnAn8P79ENK+VvgPeDScJj6UiFEMfAKcD/a+/S78HGnDNO2JcARwHzgBLQHrWuAUrTfpJ+P4jojfCf8f2G4bR+N4DX3APsABwDFwFVAaJD9WoHj0e7h84HfRx7mgMvR7slSoDx8HVIIsQC4FPi2lDIP7XPYMIbrUiQYJYKKwXhRCOEE3gfeAW6P2faolPKbcO+pGE0kfyGl7JNStgK/R/shBU0M/5+U8lOpsUZKuXGQ8/mBuUKIEillr5Ty44E7hAV5MfBrKaVHSvkF8BCaeEV4X0r5angM8Qlgj51c52nh63QDPwRqwtcFmtD/VUr5iZQyKKV8DPAC+wML0UTuyvB1e6SU7wOEr/ENKaVXStmGJgoH76Qdg3Eh8Ej4WCEp5VYpZf0oXn9juG1u4AVgTyHEjPC2s4HnpZRetB/3DVLKv0spA1LKz4GlwKkjPM9xQKOU8onw658B6tHEbSj+IKVsCUca3gM+kVJ+LqX0hNu61yiuc0yEe2sXAJeF39uglPLD8HvSDynlK1LKteF7+B3gdbQeMWj3biUwQ0rpl1K+J7WCzEEgE7AJIcxSyg1SyrWJvi7F6FEiqBiM70kpC6WUM6SUl4R/SCNsjvl7BmAGmsLhICdaj6ksvH0aMJIv/oVovYL6cDjt+EH2mQp0Sil7YtZtROuFRWiO+dsFZAkhTELLEuwN/3stZp9npZSFaE/wq9B6BbHXdnnkusLXNi3cjmnAxhjBjBIO7f0jHAbrBp5EG3McLSN974Yi+jmF37NX2P5wciZa+Be069xvwHWeDVSM8DxT0T6HWAZ+LgNpifnbPchy3BOxhBDXxNwDf0H7TLIYwXsshDhGCPFxONzpRHvwi3ymdwNrgNfDodKrQXsYAn6BFgVoDd8TU+N9XYrxo0RQMVpibUc2o/WOSsKiWSilzJdS7hqzfc5ODyhlo5TyTDTxvAuoDYfpYtkGFAsh8mLWTQe2juD4T4VDZLlSymMG2d6O1vO7UQhRGdP222Kuq1BKmR3u6WwGpovBk05uR3uPvhUO734fLUQ6WoZ77/qA7JjlwQRroD3MM8CZQsuAzUJLBIqc550B15krpfzJEOceeNxtaEIay4g+lxHQ7zrDYejSIfYd1g5HSnl7zD1wMdAOeNjJ/SmEyETrGd8DlIcfml4l/JlKKXuklJdLKWejheN/FRn7k1I+LaU8EO39kWj3tkJnKBFUjBkpZRNaaOheIUS+EMIgtMSQSPjvIeAKIcQ+QmNuTEguihDi+0KIUillCHCGV/cbm5FSbgY+BO4QQmQJLUnlQrSeVjyupQH4D9q4EMDfgIuFEPuF254jhDguLMLLgSbgzvD6LCHE4vDr8oBeoEsIUYU2pjcWHgbOF0J8N/y+VgkhqsPbvgDOEEKYhZb8UzOC472K9mN8M1pWbOT9fRmYL4Q4J3w8sxDi20II6xDHaQFmDzjufCHEWeFe9+mALXzc8bIarTd/nBDCjJalnDnEvm1o98zsIbb3I3z9jwC/E1rClVFoyTQDj58RPmcbEBBCHIM27g1Ek6fmCiEE0IUWBg0JIRYIIQ4LH8+D1sMdbLxRkWKUCCrGy7loPxR1aEkltWhjJEgpnwNuQ0u66AFeRBtHHMjRwDdCiF60JJkzBoRgI5yJloiyDW3s6AYp5ZtxvJa7gR8JIcqklCvQxgn/GL6uNcAPAMJjjiegJQNtQkuMOD18jJvQppZ0oYUgnx9LQ6SUywknYYSP9Q7be1zXofVgHOHzPT2C43nDbTk8dv9wqPRItFDpNrSQ8l0MLTb3ATVCCIcQ4n4pZQfauOLlaIlFVwHHh3vX40JK2QVcgvYwtRWtZ7hliH1daPfaB+Gw7v4jOMUVwNfAp0An2nX3+00Mvz8/R0vWcgBnoSVdRZgHvIn24PMR8Gcp5X/R3r870XqczWhRjt+MoE2KJCOkMtVVKBQKxSRF9QQVCoVCMWlRIqhQKBSKSYsSQYVCoVBMWpQIKhQKhWLSokRQoVAoFJOWtKswX1JSImfOnJnqZkxaQqEQwWAQs9mc6qakHCklgUAAk8mENk1MEW/8fj8GgwGjccJYSioSxMqVK9ullEMVUxiStBPBmTNnsmLFilQ3Y9LS29tLd3c3FRUVGAyTO5AQCoVobm4mPz+f3Ny4V/pSAF1dXbhcLsrKypQQKoZFCDFYXeKdMrl/xRSjJiJ8an4p0R6K3+9PdVMmLDk5OUgpcblcqW6KYoKiRFAxKiJhPyWCGmazWYlgAjGZTGRlZeFyudQ9p0gISgQVo0KJYH/MZjOBQEC9HwkkJyeHYDCIx+NJdVMUExAlgopRERHBUEjVAgaiCUKqN5g4MjMzMZlM9PX1pbopigmIEkHFqFBjgv1RIpgccnJy8Pl8+Hy+VDdFMcFQIqgYFSoc2h+j0YjBYFAimGAsFgsGg0H1BhVxR4mgYlQoEdwRlRyTeAwGAxaLBY/HQzAYTHVzFBMIJYKKUaHGBHdEJcckh8h0CdUbVMQTJYKKUaF6gjtiNpuj1WMUicNkMpGdnU1fX5/qDSrihhJBxagQQiCEUCIYg0qOSR65ubmqN6iIK0oEFaNGiWB/jEYjQgglgklA9QYV8SZhIiiEeEQI0SqEWDXEdiGEuF8IsUYI8ZUQYu9EtSVVTFShUCLYHyGESo5JIpE6rao3qIgHiewJPgocPcz2Y4B54X8/Ah5IYFuSjsfjoampaUL+MBoMBpUYM4BIcowi8ZhMJiwWi+oNKuJCwkRQSvku0DnMLicBj0uNj4FCIURlotqTKiZij0n1BHfEbDYTCoWUECYJ1RtUxItUWilVAZtjlreE1zUN96K65jZOeeI9KktmYcowIMwgTBJhkhjMEmHSlg3RZYnBJBARuY+xfYt1gBvODa7/fmLQ9QP3D4VCeANeMh1ejEZDzLbBXzVUW4Y+R0w7xMBtQ7dr0NePYP/Y83h8HpBg6XTvsOd4riP2HCNt11DXNOLXiMH3Gu11hEIhXME+spzNmE3mYc4xgjYl4TrG1JYRfHd2PMc47o1+59vxOC5jEF93OwX4EOFKRqN9T4e730b03Rtkf4MQ7JmZQ54x7ZzqJiVp8SkJIX6EFjKFykpeWPc2GeszmZExi5mZc6nKmIZJDH8pIUOIkEkiTZKQKUTIGP4/vCxNkpBx+3J0vVESMvffXxrl8L8osXi847t4PdOjnsJ3oE9Z/iQdR0+qW7ADe2flcm/5nFQ3QzECUimCW4FpMcu7hNftgJTyQeBBgIKplbLE4GVdCBq99TR668k0mqkunE11/mxm5syAoJmAH/wx/wwhAwYfEKfSgyYzmE3a/yYzmMP/m0za30ajJBjyYMnOICvLuH2/8GuMMa8RhsF7C7Lf39uXhopEDlwth9wmd7pPv/1l7J8St9tNIBAgLy9vmHOMsh1yqH2GO+7Or6Pf/sOeY/BXjej9CS/19bkQQmCxWEbXrtFex8DlAZ/Pzl4z2vtix3PsvF1DXdNQ++94jpFdh8/nIxgMkpmVFZ3D2n//AVeYhOtY4e7hM08vn7l72NuSN8SrFHohlSL4EnCpEOIfwH5Al5Ry2FAoQHeToOviM2h461aWrm+mNmDj82AlX3Y08GVHA9lmM8fNm8eSPawcN38+uRkZSCnxBcDjl3h8sv//fom337r++3nDf7tjln0BCPi1f7iHaqkALENt7IfRAJlmQZZZkJUhyDIT/l9s/z/m78zY9dHt219jMIy0mzo2umQXbrebivzShJ4n3XDixOPxUFFUkeqmTBoCgQBtbW1kZ2RTUFCQ6uYA8ISzhUe6mnmkq5m9snIHFWeFfhCJSnAQQjwDHAKUAC3ADYAZQEr5F6HdGX9EyyB1AedLKVfs/LhTpZTbtIWm5fDBdaxb/ylLA1ZqQ7uzPLD9ByjLZOLouXNZYrVywvz5FGRlxeXagqHt4jhQND0+bZvLG6TD2QeGTIIYt4uqj6j4RtYF45xomWEaKKoDRDSDHdbtsF94OcPEDl/i7u5u+vr6qKyccHlM46Kvr4+uri7Ky8sxGo2pbs6kwel04na7KSsr08X77goFOXOrne5QkLvKZrHQkp/qJk0KhBArpZT7jvp16Zbl108EI2x5F97/LWx9n02hAp43LKTWfDAfOLZn6mUYjRwxezY1NhsnLlhAsWVkvbSxEgqFaG5uJj8/P5rJNhT+YEzPM6aH6vZJPD40wR2iFxsR3e1/Dx22GQtCEO59EhVIsyGEkQAF+RYsZkHmIEKbFSO0mTFCazZO3Kdin89He3s7xcXFZMXpgUuxc6K9wWz99Aaf6WrlQWcTCzIsPFAxT/UGk8DkFkHQgv0bX4f3r4UWrUO5LXsBL1ReTK2zgHc3bSYUvlaTwcBhs2ZRY7XyvepqSnNy4t5OKSVNTU3k5eWRl5e8cYFQJPQ7iKgOHgpmUGGN/O+P8zQso4Edep6ZGWDptzx0L3b7dpIS+h0NkQefZH/mCv31Bt2hIGdvrccRCnBr6UwWZ+tDnCcySgQjSAlrlsGH10F7uFhN4Vxa976WF4M2au31vL1+PcHwdRuE4OAZM6ix2Ti5uprKOP54NTU1kZOTQ35++oZDgqGBAgldvW46nX1kZRfgC4rBe6bRdfQbX01E6HdgWHfHUDAxoeD+wmqJGWcdLPQ7WlpbWzGZTBQXF8fpChUjQY+9wdruNv7k2MYccxYPVs7HoHqDCUWJ4EBkCOr/CR/dAI5Gbd2UXWHxzXRMPYqXVq+m1m7njbVr8Yernwhg8fTp1FitnGK1Mm2cX6bm5mYsFotuvpTxwu1243A4KCsrw2QaeW6VlJJAcPieZ6xoDkxOGri/N87FeCKh337h3EFFdaDobt/X6+rGIP1MqyrDNIFDv3pEb71Bnwxx9tZ62oN+biyZwcE5halu0oRGieBQhALwzePw0U3Qs0lbV7Y3HHgrzDwap9fLvxoaWGq38+81a/DGlGHaf5ddWGK1ssRqZVZR0ajb2tLSQmZmJoWFE+vm93g8dHZ2UlJSQkZGRsraEZIS38DEpEGSlAYV2EHCw4E4h35NBgYX0Ywde6+x4rrj/prQqp7E8ASDQVpbW8nKyqJoDN/XRLCsp53/69zKDHMmD1cuwKg+w4ShRHBnBLzw9UPwya3Q16ytm7pYE8NphwDQ4/XySmMjS+12Xlm9GndMCax9KitZYrVSY7Mxb8qUEZ2ytbUVs9msmy9kvPB6vXR0dDBlyhQyMzNT3Zy4EQgOFE0t/LtDD3QwIY3s6wvh9oXwBQShOH+1Mk0xorqz8dIheq3bE5TGH/rVIz09PfT09KT8AS2CX4Y4Z2s9LUE/10yZzhG5E+u3QE8oERwpfhd88WdYfid4OrR10w/XxLByv+hufT4f/16zhqV2O/9avZpe3/ZZ9ruXl0cF0VY69Fy5trY2jEbjhBsf8vv9tLW1qSzIQYhNjsm05A47XuoO906HFtXt+3vjXJLUIGLGSQdk8+4oqgNFt39PNtMsdBP6lVLS2tqKwWCgpKREF0L/Sk8H93RuYRdTBo9OrVa9wQShRHC0eLvhs/tgxT3g69bWzT4BFt8MZXv229UTCPD62rXU1tXxUkMDXd7tpdCsJSXU2GwssVrZvby835euvb0dIQRTRthzTBcCgQCtra0UFRX1q46i0GhtbcVoNMb1cw9JbQx0pOOlkfDvoNt9kkCcE5RMxh2zfreHdsPZv8MWe4iZmzrO0G9kzLqwsJDs7Ow4XuXYCEjJedvq2RbwcdWUaRyTO7EeivWCEsGx4u7UhPCz+yAQrvs4/zQ44CaYUr3D7r5gkLfWraO2ro4XGxrodG8vGTO3uJgaq5UlNhv7VFbS2dmJlJKSkpL4tVcHBINBWlpadPMjoze6urpwuVxUVFTooicyGIHg4OOigwntkKIbEyqO989IbLLRzsZLs8wDkpgyBO5eJ0YCTJtaSobZkPLP4Y1eB7d3bKLCmMHjVQswC+VnHm+UCI6XvhYtRPrlAxD0agU9befA/tdD4exBX+IPBnln40Zq6+p43m6nzbW9ePKMggKOnz2bY2fN4ujddptQSQ2ROZAjKQQwGdFL4lCyiJQl3FlRh+HKEsau88U59DtoWcIBojlYKHj79v6hYuMY5qYGpeTCpgY2+r38sngXTsybWNEhPaBEMF50b4ZPboNVD2uZpQYTfOsi2O9ayKsa8mXBUIj3N22itq6OpXY7Tb290W1VeXnRMcQDpk3DaEj/p8CJOv0jHqhJ8+MjFNo+x3S48dIdk5T6C6zbF8LrkwRlfB9AzcaB46kDRXSQ2r8ZAnuwj0f7mijMMPKHXeaQl2kkMw5zUxUaSgTjjXOtNq2i7klAgjET9rwEFl4N2WXDvjQkJR9t3szTX3zBssZGtsYIYnlODqeEBfE7M2ZgSlNBnKhJP/Gira0NIcSEC4WnE6FQiNbWVoTBTHZe0U5Fc2cVliKh4riWJSQc+h1WVAeOpw4eKp7oZQl3hhLBRNFRBx/eCKuf05bNObD3ZbDvFZA1fLpzZGxocyjE0ro6au121jkc0e0l2dl8b8ECamw2Dps1C7MOJviOlI6Ojgk53hkvIkXG9TwuOBno7e2lu7s7bpnMI3Gkcfdb7r9vhzdAs8ePKWAgM2TEn4DQ72BlCYdzpKmaYmRGaVpYyw6LEsFE0/I5fHg9rHtZW84s0IRw78sgY/CQ10C3BSklXzQ3s9Ru57m6OlZ3dET3LcrK4sSwIB4xezaZo6jEkgqcTider5fy8vJUN0WXTNS5lOmGlJK2tjYASktLU/5AIqXk4uZGVvvcXFxYSU1e6U4daYYOBcfHkcYg4PbvFzAlL30ewgdDiWCy2PYRfHAdbHpLW7aUaCHSPS4Bc//pApGJu1OnTt3hMFJKvmlri/YQV7W2RrflZ2Zywvz5LLFaOXruXCxmc0IvaSxErq2ysjLlPyx6REpJc3Nz2teOnQhEHkj0ksj1sbub37Sup8Bg5OkqK9mG+IjPzssS7pi8ZN/ip7UrxA8Oy2FxdXo/rCkRTDab/qvZNzV9pC3nVML+12pJNEYtIzASihmJUNS3t7M0nFTzeXNzdH2O2cxxYUE8dt48cnWSbehyuXA6nco7bxg6OjoIhUKUDlNQQZEcOjs78fl8lJaWpvx+lVLys5Y1fON1cWFhBd8vSF005Y0vPTz7gYvF1Rn84LDUPyCMByWCqUBKWP8afHAttH6urcufAYtuANs59Lm9dHV1UVFRgWEUCTBrOztZardTW1fHp9u2X2uWycQxYZPg4+NoEjwWJts0gLEQ6S2P9vNXxJ+Iy4TFYtFFLd+V7h6uaF1HXrg3mBun3uBo2dga4NbabkrzDdz+/dS/L+NBiWAqkSFofEEbM+yo09YVzce7zzV0lHyX8orKMT99bnQ6ed5up9Zu58PNm6PrM4xGjpwzhxqrlRMXLKAoyZVbIqXTVNWYoVEmu/qiu7ub3t5eXYzTSin5ZctavvT2cV5BOT8orEhJO4IhyS8eduDxw/87t5Ci3PR9WFMiqAdCQah/Bj68AbrWAeAvsGI46FaM80/WvHrGwdbubl6or6e2ro53N26MpmqbDAa+O2sWNTYbJy1YkBCT4IFE5sIVFBSQk4TzpSORcUE9edxNZvSWJPOVp5fLWtaSIww8XWUl35iaZLj7Xu5h1SY/Pzwih4Xz0ndcUImgngj64ZtHkR/dhOjdqq2r+DYsvhVmHDFuMQRo6e3lxfp6au12/jvAJPiQmTOpsVo52WqlIoGJABPBNDjRdHZ2EggEKCsbfm6pIjlEeuc5OTm6eDC5smUtKzy9nJVfxg+LKlPShldWunnxEzeH7JbJ2d9J3wdaJYI6xNvXhWf5/eTb/4hwh7M/d/mOJoa7HBS387S7XLzU0EBtXR1vrlvXzyT4wOnTqbHZOMVqZZc4i9VEtYqKJ5HkKJVApB+6urro6+vTxXh2nbePnzavIUsYeLqqmiJj8jPBG5v8/L8XeqgqNnLjGal/MBgrSgR1SOSpc0p+Fpl1f4NP7wJPeLL8zKNg8S1aDzGOOD0e/tXQQK3dzn8GMQmOFPieGYfkgPb2dgA1YX4YImOnqti4ftBbWPQ3rev42N3DqXmlXFK843SqROMPSn7+kINAEH5/QSG5Wek5LqhEUIfs4Lvn7YKVv4eVvwNfj7bT3O/BATdD6bfifv5ur5dXVq9mqd3Oq42NO5gERyygRmoSPBCHw4Hf71ehvp3Q3NxMVlaWLrISFRqRuYN6CIuu9rr4cXMjGULw1FQrJabk9wbvfrGb1dsC/PSYXPaclZ7Z3koEdciQvnuudlhxN3z+Bwi4AQHVZ2j2TUXzEtKWPp+P18ImwS8PMAneI8Yk2DqKOW0DK+IoBsfhcODz+VR1HZ2hp7Do9W0beM/Vxcl5U/h58S5JP/+y5S5eXuHhiD2yOG1xekYslAjqkJ367vU2wfI74Ku/QtAHwgi7ngeLrtfmGyYIt9/P62vXstRuZ1lDA90xJsG20tJoyPRbZWXDhor6+vrGNA9yshF5n8rKyjDpvBzeZEJPYdH1PjcXNq3GhOCJqmrKTckV5brNfn7/rx5mlBq59tT0HBdUIqhDRjyNoHsjfHwrrPo7yCAYzLD7j2C/30JuYntZ3kCAt9avp7aujmUDTILnFRdHQ6Z7D1L1JjJhvrS0FLMOS7vphUhEQE0n0R96Cove0raRt11Ojs8t5vIp05J6bo9fctlDDiRw/4VFZGWkXylEJYI6ZNTms45Gzb7J/jQgwZQFe14K3/41ZCc++cQfDPK/DRuoravjhfr6fibBMwsLoz3EhVVVGIRQk8FHQUtLCxkZGSqTVofoJSy6ye/h/G0NCODxqdVMNSd3zt7ttV2sbw3yi+Pz2HV6+j3UKhHUKdu2bRu9uWr7Km3CfePz2rI5F/b5Jex7ueZekQQCA0yCm2M8EXfJz2eJ1crJCxYwJyODosJC1cPZCU6nE4/HQ0VFaiqDKIZGSqn5DgqR8rDoHe2beL3PwVE5RVxdMj2p537uQxevf+Hh2H2yOHm/9BsXVCKoU8Y1obxlpeZYsf41bTmrCPa9Evb+ueZrmCQiJsG1YceLLd3d0W1lFgsnzp/PWXvswUFpbBKcaCIFx1XoWJ9EwqK5ubkpLf6w1e/l3G31APx96gKmm5MXYflyg48/vtrLvEoTV52cfgUwlAjqlObmZiwWy/jGG7a8rxXp3vKOtpxdBgt/A3tcrIVMk0hISj7dujVa4Hu90xndVpKdzcnV1dTYbBw6c2ZamQQnmkiSlF7sfBQ7EgmLpjq8f0/HZl7p7eSw7EKuK01cgtxA+jwhfvmIE6MB7r+oCLMpvcYFlQjqlJaWFjIzM8c/R0xKzcPw/d9C83JtXW4V7H8d7HYBpKDShJSSt+vreWXtWl5ev57Gzs7otqKsLE6qrqbGauXwNDAJTgatra2YTCaKi4tT3RTFIEgpaW9vJxgMptRyqSXg45yt9QSQPFw5n1kZyStQf9M/u9jSEeTK7+Uxf2p6RSyUCOqUuJcWk1Jzt//gWmj7SltXMFuzb7KeDUm2ZInUxiwtLWVVa2u0h/hNOPUctpsE19hsHDVnji5NgpNBV1cXLpeLioqKlFcpUQxOxHLJbDYzZcqUlH1O93Vu4cWeDr6TXcBNpTOTdt6n3+3jv6u8nLTQwvH7ppc7jBJBndLW1obRaIz/078Mwepa+OB6cDRo64qtsPhmmHcKiOSMzXV1deF2u3dI+LC3tbHUbmep3c4Xg5gE14RNgnMmkReh2+3G4XCkPAtRMTyRzymV44PtAT9nb6VrvLYAACAASURBVLPjk5K/Vc5nbpJ6g5+u8fLg633Yppn45QnpNS6oRFCntLe3I4RgyhhLk+2UUADsT8GHN0L3Bm1d6Z5w4K0w69i4OFYMR6RA9HAT5td0drI0nGUaaxJsMZk4eu5camw2jp8/n/wUe7wlmsi80VFnCyuSjh7GB//UuZXannYOsORzW9mspJzT2RfiysecZJrgvouKMBrSJ2KhRFCndHR0IKVMfJHpoA9WPQIf3wK94fencpEmhtMPS9hpI0/NI62GsiFsErxUZybByaKtrQ2DwZC4hyJFXNDD+KAj6OesrfV4ZIg/VczFlpmcjPDfPuWktSvENUvymVWePmP5SgR1SmdnZ/SLlBT8bvjqL/DJHeAOj8tNOxQOvA2mLor76SKp5WNx697a3c3zdju1djvvDTAJPnz2bGqsVk6qrqZkArkvROqtqnFB/RMIBGhvb8dkMqVsfPBBxzae6W5j36xc7i6fk5RzPvbfPt63ezn1AAtH7pk+D6NKBHVKypwWfL3w+f3w6d3gDU9jmHWs5mVYvlfcThMpCTZeq6DmiElwXR3/27AhahJsjJgE22ycXF1NeZpPLxjPQ4Mi+UQiHakqq9YVDHDWVjsuGeL+8jl8Kyvx9/+H9V7+/nYfe8w0c+mx6RO2VyKoU5xOJ16vN3UOAh4nrLwXVv4f+MNVX+Yt0RJoptjGffhIabh4jnO1u1wsq6+n1m7nzXXrCMSYBB80YwY1ViunWK1UpaGjvZSS5uZmsrOzU16rUjEyIuODO7jBJIm/O5t5vKuFPTNz+H3F3ISfr607yDVPdpGdKfj9BYUY0iRioURQpwyVPZl0XG2w/C748k8Q8ABCm1JxwI1QOL4wS1wKAgyBw+3mX6tXU1tXx3/WrsUXYxK8aJddogW+Z6SRV5/D4Yg+GKmQqP6RUtLR0UEgEKCkpCTpTiC9oSBnbrXTGwpyb9ls9rYktncmpeTXj3fh6Atx4+n5VE1Jj3FBJYI6RXeeez1b4ZPb4eu/Qciv2TftdoE26T5/bJXrEzYNZADdXi8vx5gEe2JMgvedOjVa4HuuziejR9w3VEg0fQgGg9H7vKSkJOkPL092tfCws5ldM7P5Q/nchJ//b2/0srzRx1nfyebQ3dKjOL4SQZ3S09NDT08PlYNYEaWUrg3w0c1Q95g259CYAbtfDPtdAzmjC90mPfkH6PX5eK2xMWoS3Of3R7ftUV5Ojc1Gjc1GdaKzcseAlJKWlhblNp9mRMZzs7KyKCoqSur32RXuDXaHgtxVNouFlsQOBbzzjYcn33Hx7bkZ/OjI9BiHVyKoUyLz6HQnghE6G7Q5hg3/0JZN2bDXz+DbV4FlZD2qVDskuP1+/hM2CX5pCJPgGpuN3XZiEpxMVEg0PYkYJKdiIv0/ulr5q7OJBRkWHqiYl9D7pqkzyPX/6KIgW3D3eYVpcY8qEdQpaeO+3vaV5lix9iVtOSMf9vmVZuGUOfyXXU+9XW8gwJvr1lFrt7Osvh6HxxPdNn/KFJaEBXGvFE9RiIREU12sWTF6IokyyTZJ9oRCnLXVjiMU4JbSmRyYnbjEKiklv/q7k16P5LazCygr0H8xfCWCOiVioVNeXp6ygryjomm5JoYbX9eWs4o1U9+9LgXz4FMg9HqN/mCQ/8aYBLfHmATPKiyMCuLCqqqkC6IKiaYvUspoT764uDip47q13W38ybGN2eYs/lY5P6GZmw/8u4fP1vk579AcDrTqf+xaiaBOGW1FFd2w5V3NsWLr+9pydjns91vY/Udg6v+FiIyV6LkmZiAU4r2NG6mtq+P5+vp+JsHT8vM5JSyIB0yblrSU8EgYWYVE049QKERHRwfBYJApU6YkzSPSJ0OcvbWe9qCfG0pmcEhO4h6g3vzSwz8/cHFAdQbnH6b/cUElgjolEvZKSzNVKbUe4fvXQssKbV3edFh0PdjOjdo3+f1+2traUjaParQEQyE+2rKF2nA901iT4MrcXE6xWllitSbcJDjy8KBCoulJMBikvb0dgJKSkqRFQV7qaef3nVuZYc7k4coFGBP0ALWxLcCtz3VTmm/g9u/rP1qhRFCnpEMvaadICWuWwYfXQfsqbV3hXDjgJlhwOiEEzc3NaWkYG5KS5Vu3srSujlq7nQ0xJsGlMSbBhyTAJDgSEs3MzIyf1ZYiqfj9ftrb25NqveSXIc7d2kBz0Mc1U6ZzRG5i7p1QSHLZww48frjr3EKKc3Wc04ASQd3i8/lob2+fGHPCQkFoeBY+ugEcjdq6KbvC4ltoytmP7BSVlooXUko+a2piqd3Oc3V1rIkxCS62WDhpwQJqbDa+O2tW3EyCnU5ntJiCCommJ5Foj8ViSdrDzKu9HdzdsYVdTBk8OrU6Yb3B+17uYdUmPxcdkcN+8/T9+6VEUKdEQoUTKuQVCsA3j8NHN0HPJgD8xXvg2fe35O1Wk3D7pmQgpeTr1tZoD7FugEnwiQsWUGO1cuQ4TYIjkYJ0CSUrBicyFSpZUyeCUnLetnq2BnxcNWUax+QmpkDEa5+5ef5jNwfvmsn3D05eJuxYUCKoUyIFpifkj1zAC18/BJ/cCn1h49yqA7Ui3dMOTm3b4kxdW1vUE/HLlpbo+tyMDI6bN48am41j5s4dtUmwColOHCJTJ5I1LPBGr4PbOzZRbjTzRFU15gQYaa9p8nPXCz1UFhm5+Ux9R3mUCOqUYDBIS0vLuF0WdI3fhfvje8n88v8weMMhxOmHa16Glfultm0JoLGjg6VhT8QVA0yCj5k3jxqrleNGYRLc1dWFy+VSIdEJgMPhwO12J2UOYVBKLmxqYKPfyy+LqzgxL/7VkQJByc8fcuAPwu/OLyTPot9xQSWCOiXiJp7sibXJpru7G5ezmYqt/4QV94AvnHE5+wRYfAuU7ZHaBiaIDU5ntIf40ZYt0fWZEZNgm40TFyygcJhQuAqJThwicwg9Hk9SHnz/1+fkpvaNlBjNPFVVTUYCeoP3vNhNw7YAlxydy16z9Zvcp0RQp0SshtIxc3I0RCrjlJeXY/R1aUL42X0QCE9Qn3+alk06pTq1DU0gWyImwXV1vL9pU9Qk2Bw2CV4yhEmwlJLW1lbMZnPCi5ArEo+Uks7OTrxeb8IfbEJS8sOm1azze7i0aCpL8uNfv3fZchcvr/Bw+B6ZnL5Yvw/ySgR1zLZt2+Lqt6dHBp0P2dcCy++ELx+AoBeEQZtfuOh6KJiV2gYnmObeXl6w26m12/nfhg2EYkyCD501iyVWaz+T4EhItLy8XN/l9RQjIiKEPp+PoqKihCbFve/q4rq2DRQZTDxdZSUrzvePfYuf373Uw4xSI9eeqt9xQSWCOqapqYmcnJykF9xNJsNmwXZvhk9ug1UPa5mlBhN86yLY71rIq0pNg5NIW18fyxoaqK2r46316/uZBH9nxgyWWK0cP3cumUnoOSiSRygUorOzE7/fn9DyalJKftLcSIPPzcWFlZxeUBbX43v92nzBkIT7LizCkqHPcWslgjomkaazeiGSADTs2KdzrTatou5JQIIpC/a4BBb+GrLj+8XVK51uN/9qaKDWbuf1ASbB3y4v56R58/j+PvuklUmwYmgi5dUCgQBTpkxJWMGMT9zdXN26ngKDkaerrGQb4lvY4Y6lXaxrCXLZ8bnsNl2f44JKBHVMJAV+ohdKHnGPt6NOs29a/Zy2bM6BvS+Dfa+ArMkzTaDL44maBL+2Zk0/k+BvT51Kjc3GEquVOWqcMK0JhUK0t7cTCoUoLi5OiBBKKflZyxq+8bq4sLCC7xeMzhN0Z9R+6OI/X3g4du8sTt5fn1nuSgR1TCTpYaLPAxu12Ld8rpViW/eKtpxZoAnh3pdBxsQdPx2MXp+Pl+x2/vHVV7y1eTOuGJPgPSsqop6IC3RoEqzYOcFgkI6OjoQK4Up3D1e0riPXYOSZKiu5cewNfrnBxx9f7WVupYlfn6zPYR0lgjqmra0No9E44TP/2tvbEUIwZcqU0b1w20eafdOmt7RlSwks/A3s8RMwT67xsZaWFnxSssLhiJoE9/h80e27lpZSY7NRY7Oxa2mpmleYRkSEMBgMJiRZRkrJr1rW8oW3j3MLyjm/MH4m132eEL98xInRAPddVESGSX/3nRJBHTNmcUgzHA4Hfr+fsrIxju9t+q9m39T0kbacUwn7X6sl0Rj1OQ4Rb7q7u+nr64tmiXoDAd5Yt47aujqWNTTgjDEJXhBjErynmmifFsSOERYWFsY9CeprTy8/b1lLtjDwdJWVAmP87Ntu+mcXWzqCXHFSHguq9OeIo0RQx3R0dCClpGSCh7IiP+CVlZVjP4iUsP41+OBaaP1cW5c/ExbdALbva5mlE5hIlu1g80p9wSD/Xb+e2ro6Xmxo6GcSPLuoKCqI3546VQmijolkjfp8voRMqL+yZS0rPL2clV/GD4vG8V0cwDPv9fH2115OWmjh+H31F6FRIqhjOjs7CQaDlJbGfyKrnogUEa6oqBj/XDcZgsYXtDBpp11bV7QgbN90qjbncIISSaIoHSbcGQiFeDdiEmy309LXF902vaCAU8IWUIuSaBKsGDmxE+rjXUjD7nVxSXMjWcLA01XVFBnj02tbudbHX/7Ti3UXE786UX/jgroUQSHE0cB9gBF4SEp554Dt04HHgMLwPldLKV8d/pjpJ4LjDhOmCW63G4fDEV8D4VAQ6p+BD2+ArnXautLd4YBbYM4JE8KxYiCR93GkziPBUIgPN2+OmgRv7emJbqvMzWWJ1coSm42Dpk/HqCbi6wYpZdRKK97FNK5pXc9H7m5OzSvlkuKpcTlmtyvE5Y86yTBp8wVNRn1993QngkIII7AaOALYAnwKnCmlrIvZ50HgcynlA0IIG/CqlHLm8MdNPxF0Op14vV7Ky+Obtqw3EuqdGPTDN4/CRzdDb7hGZ8VCzbFixuETSgwjZdRMJtOox5EjJsG1dXXU1tWxsasruq0sJ4eTq6tZYrUmxCRYMXqklNFqQTlx9ONs9Ln4UVMjGULw1FQrJab4PJRe+7STFmeIa5bkM6tcX0MTYxXBRD4WLgTWSCnXSSl9wD+AkwbsI4FIv7oASC91GyFCCNIt7DwWjOEf1WDMBPD4HdwMu/8QLmyEQ+/TJtc3L4elR8Kzh8CW9+N/zhQhhCA7Oxuv10sgZu7gSDAIwf677MI9Rx7J+ssuY8UPf8jVixczt7iY1r4+/rpyJUc++SQV997LhcuW8WpjY78J+4rkIoSgsLCQnJwc+vr6cDgccfmtmJeRzUHZBfik5Knulp2/YITMr9TEdPU2/072TB8S2ROsAY6WUl4UXj4H2E9KeWnMPpXA60ARkAMcLqVcOcixfgT8SFuq3CfdeoJxSRhJAyLFwpNSJ9XfB5//ET69CzwObd3Mo7SeYcWoHwZ1RygUoqWlhezs7Lj0DqSUfNXSwlK7nefq6qhvb49uK4iYBNtsHDlnDlkmfT3hTxZ6enro6ekhIyOD4uLicY+rr/e5ubBpNSYET1RVU24af4b1Rw1eHnmrjz1mmrn0WH3N5dVjOHQkIvircBvuFUIsAh4GdpNShoY+bvqFQyM3d2Vl5YTP2kt6dRxvF6z8Paz8HfjCY2FzT4bFN0PJbslpQ4KIjBcloqh2XVtbdAzxqwEmwcfPn0+N1crRYzAJVowPt9uN0+mMzis2jfOB5Ja2jbztcnJcbjFXTJk27vZ19AS5+okusjMFv7+gUFdJV3oUwUXAjVLKo8LLvwGQUt4Rs883aEK5Oby8DthfStk69HHTTwTjmjWpc1JWGMDVDivuhs//AAE3IKD6DC2btGhectsSJyLTJRLtRbm6oyPqibiyqSm63mIycey8edTYbBw3bx55CSoAreiPz+ejs1Mzpy4qKhrX+Pomv4fztzUggMenVjPVPP7P8NePO+nsDXHDafnsUqKfqIEeRdCElhjzXWArWmLMWVLKb2L2eQ34p5TyUSGEFXgLqJLDNCodRbCf194ET0ZI+XSQ3iZYfgd89VcI+kAYYdcfwKLrIH9Gato0DiLTJZKVWbw+XKlmqd3OxwNMgo+aO5caq5UTdmISrBg/gUAg+l0qKCgY11zCO9s38Z8+B0flFHF1yfRxt+2hN3r5pNHHmQdlc9i39HMf6E4EAYQQxwL/hzb94REp5W1CiJuBFVLKl8IZoX8DctGSZK6SUr4+/DHTTwRdLhdOp5OysrJxhzf0TldXF263m4qK+JVsGhPdG+HjW2HV30EGwWCG3X8M+10DuekzNjva6RLxZHNXl2YSbLfzwQCT4CPmzNFMghcsYEqC3dMnK6FQCIfDgdfrJTc3d8xWbNv8Xs7dVo8E/j51AdPN47uP3v3GwxPvuNh3TgY/Pko/RuG6FMFEkI4imJD5czolEvrVzfino1FzrKh/Bs2+yQJ7Xgrfvgqy9V/BZzzTJeJJU08PL9TXU1tXxzsbN/YzCT4sYhJstVKWwLDtZERKGU2sy8rKoqioaEzfq3s6NvNKbyeHZRdyXen4IiJNjiDXP9NFQbbg7vMK9fE9R4mgrom4rpeUlCTMT0wv6LbX275Km3Df+Ly2nJEHe/8S9v2V5l6hYyKJVXp5T9v6+nixvp5au523Y0yCDUJETYJPsVqZmugM4UlEZEgl4kYz2vugJeDjnK31BJA8XDmfWRljL3smpeTyR530uCW3nlVAeaE+hniUCOoYr9dLR0dHYiaR6wzdX2vLSnj/Wtjwb205qwj2vQr2/pnma6hDgsEgra2tcZsuEU863W5eamigtq6ON9ati845FMAB06ZRY7NxitXKdJ21Ox3xer04HNp0oMLCwlGHx+/v3MILPR0clF3AzaUzx9WWB/7dw2fr/Jx7SA4H2fTxPVciqGMilVRSMa6TbAKBAK2trRQVFcW9Qn5c2fK+VqR7yzvacnaZNl64+481x3udkcjpEvEiYhJca7fz7wEmwQurqqgJl2+bPcF9NRNJMBjE4XDg8/nIzc0lLy9vxOHIjoCfs7bZ8UnJg5XzmJcx9rHct77y8I/3XSxakMEF39XHuKASQR0TSXXXvTDEgciE+XgXBU4IUmoehu//Vqs+A5C7i5ZJuuv5WpUanRB5kEr0dIl40evz8WpjI7V1dbzS2NjPJHivigpqbDaWWK3KJHgMxI4TZmRkUFRUNOKs8z93buO5njYWWfK5vWzWmNuwqT3ALc92MyXPwJ3nJGlO8E5QIqhjIr2jRNim6JHm5mYsFovuQndDIiWse1nrGbZ9pa0rmA0H3AjVZ0EcHbrHQ7KnS8QLl9/Pv9esYandzr8GmATvVlZGTdgCyqZMgkdFZGK9wWCgsLBwRMMPjqCfs7bW45Eh/lQxF1vm2B6oQiHJLx5x4vZJ7jqngOK81H9HlAjqmGAwSEtLS9o8xY+XlE2YHy8yBKtr4YPrwdGgrSu2atVn5p2ScvumSJaxbsdbR4AnEOCNtWuptdt5aRCT4BqbjRqbjT3Ky5UgjgC/34/D4SAYDJKXlzei6MvfHE083d3Kvlm53F0+Z8znvv/lHr7e5Oeiw3PYb37q70clgjomFArR3NycHiHCOJD2JsKhANif0qZWdG/Q1pXtBYtvgVnHpsyxIjJdwmw2p98DxiD4gkHejpgE19fT4XZHt80uKor2EPdVJsHDEgqFovNzIyULhwuPdgUDnLXVjkuGuK98Drtnje036bXP3Dz/sZvv2DI555DUP9wrEdQxSS0srQMmjHVU0AerHoGPb4He8D1XuQgOvBWmH5aSJkWmS0y0OaeBUIh3Nmygtq6OF+rrdzAJXhIWxP132UVX9Sr1RF9fH93d3VFniuGS8P7ubObxrhb2yMzh9+VzxvSQsbbZz53P91BZZODmM1M/LqhEUOc0NTWRk5Mz5qoP6cSEKxjud8NXf4FP7gB3m7Zu+mGaY8XURUltSigUorW1Neo0MBEJhkJ8EDYJfn6ASfDUvDxOqa6mxmbjQGUSvAOBQCBq4h2ZUjPYd7A3FOSsrXZ6QkHuKZvNPpbRP5wHgpLLHnbgC8C9PygkPzu1n4USQZ2Tdski4yAyYX7C1Ur19cLn98Ond4PXqa2bdawmhuV7Ja0ZkYeMyVB8ISQln2zZopkE2+1sGmASfEp1NUtsNg6ZOROTEkRAizz19PTQ29uLyWSisLBw0Pvkya4WHnY2s2tmNn8onzumB9Z7l3VTvzXAT47KZe85qb0XlQjqnKRbDKWQCV8hx+OElfdqFk7+cNhufo3mWDHFlvDTR3qDZrM5paXUko2UkpVNTZog1tWxNjxxHGCKxcL3qqtZYrXy3dmzyZhID19jxOfz4XA4CIVC5Obmkpub20/o3KEgZ2610xUKcmfZLPazjD5K9dJyF/9a4eHw3TM5/cDUjgsqEdQ5kR+tokkwUXjSzIt0tcHyu+DLP0HAAwiwnq1NrSgce9bdSIiU0UrnTNHxEDEJjvQQB5oEn1RdTY3VyhGT3CQ4NmkmIyODwsLCfiXX/tnVyl+cTSzIsPBAxbwR9Qbvv/9+HnjgAWw2G2s3bOXrrz7n8DOu4z9PXDvidnV2dnL66aezYcMGZs6cybPPPjvob+NVV13FK6+8QigU4ogjjuC+++4bso1KBHVO2k4bGANSSpqbmyfNGCg9W+GT2+DrhyDkB4MJdrsA9rsW8sdvZDoYkUxRo9GYvlm4cSRiElxbV8fXrdvtSHMzMjhh/nxqbDaOnjuX7AmUTDQa3G43XV1dSCn79Qo9oRBnb7PTGQxwS+lMDsze+XBNdXU1b775JhkZGTSu3cDPbv4HGZZC3n7mWrIzRxaSvuqqqyguLubqq6/mzjvvxOFwcNddd/Xb58MPP+TKK6/k3XffBeDAAw/kjjvu4JBDDhn0mGMVQRVETxJCCNLtgWOsCCEwmUz4Y6qETGjyquDwP8MFDVqlGRmCrx6ER+bCf38BfS07P8YoEUKQl5eHz+fDEzPXbrJiKy3l+oMP5quf/IT6n/6U2w47jL0rK+n1+Xhm1SqWPPsspXffzanPPcc/V62ix+tNdZOTisViobS0lKysLHp6emhvb8fv95NlMHBWvlZ84e/O5qg7yFBcfPHFrFu3jmOOOYannnqKxYsWUpyXiQTWNgeGfW0sy5Yt47zzzgPgvPPO48UXX9xhHyEEHo8Hn8+H1+vF7/cnJONc9QSTRNrPnRslE2aaxFjobNAcKxr+qS2bsmHvn8O+V4IlfpEAKSVtbVq2aqmqtjIo6xwOltbVsdRu55OtW6PrM41Gjp47lxqbjRPmz6dggtf0jcXj8dDV1UUwGCQ3N5fM3BzO2dZAW9DP9SUzODRn+LyFmTNnsmLFiuhv2WkX/paNjkx+feUVnLJIq4h10EEH0ROT1Rvhnnvu4fDDD6ewsBCnU0suk1JSVFQUXY7liiuu4KGHHkJKyaWXXsptt902ZLvG2hOcvMHyJCOEIBS2nJkMmM1mXC4XwWBwYmWIjoTiBXD8P7SC3B9cB2tfguV3whd/hn0vh71/AZnjDxNHeoMOhwO32z0pSvKNltlFRVy5eDFXLl7MprBJ8NKwSfCyhgaWNTRETYJrrFZOqq6meCKPYwNZWVlkZGTQ3d1Nb28vHo+HM3KK+UN3C491NfOd7AKMo3igmpJnYKMDVjdt7wm+9957I369EGLQB7g1a9Zgt9vZsmULAEcccQTvvfceBx100IiPPRKUCCaJyRQOBaITuf1+/+QTwQilu8P3lkHTcq0u6cY3tB7iZ/fDwl/Dnj8F8/iEy2Kx0NvbS09PDxaLRfUGh2F6QQG/2H9/frH//mzr6eEFu51au513N27k1cZGXm1sxPTyyxw6cyY1Nhvfq66esCbBkXqjFouFrq4u9nUFKRNGNvq9vN3n5IjckSfwFecaEMCG1gC+gCTDJHbaEywvL6epqYnKykqampoGrYf7wgsvsP/++0erbB1zzDF89NFHcRdBNSaYJCabCEYy0AKBkY8TTFgqF0LN63Da/6DqQPB0wLtXwcNz4PM/QmB841P5+fkEg0FcLld82jsJmJqXx08XLuS/551H0+WX89fjj+eI2bORUvLGunX8+OWXqbz3Xg597DH+tHw52wb5QZ8IZGZmUlpaSmFeHt8zaA9kf3c0RY2SR4LZJCjIMRAMwfoW7fv+3nvv8cUXX+zw7/DDDwfgxBNP5LHHHgPgscce46STTtrhuNOnT+edd94hEAjg9/t55513sFqt473kHVBjgkkikqZcUVGR6qYkjck0LWTESAkbX9eMfVtWaOvypsOi62HX87TM0jHQ0dFBIBCgrKxM9QbHQYfLpZkE2+28sXYt/rAYxJoEL7FamTYBi154fD4uaG6kSQa4OKOQk6dUDjrPNzImGAgE2Hfffenu7iYoDWDK5pHnP+f0Q3eeB9DR0cFpp53Gpk2bmDFjBs8++yzFxcWsWLGCv/zlLzz00EMEg0EuueQS3n33XYQQHH300fzud78b8phqioTOifh/VVZWpropSSNSvindrH+SgpSwZhl8eB20r9LWFc7VJtxXnzFqx4qI3+BkKdKeDJwRk+C6Ov69Zg3eYDC6bb+qKpZMQJPgN3od3N6xiRIM3GsqJi87m/z8/J0Oaaxc6+Mv/+nFuouJX52YmmlRSgR1TqTU1dSpU1PdlKQRueaKigrduqGnnFAQGp6Fj24AR6O2rmQ3OOBmmPu9UTlWdHZ24vP5KCsrU+93nOnxejWTYLudVweYBO9dWUlNWBDnp3kFn6CUXNjUwEa/l59kl3BQwIAQgtzcXHJycoaMMnS7Qlz+qJMME9x3YREmY/KjEUoEdU5vby/d3d0Tp6j0CJjw5dPiSSgA3zwOH90EPZu0deX7aHVJZx41IjGMVOrJzc2dHEUKUkTEJLi2ro5/rV5Nb4xJ8LfKyqKeiLbS0hS2cuz8r8/JTe0bKTGaeax8Lp4eLYPUZDKRn58/pDvFdU87aXaGuPqUfOZUJD/nUomgzomUS4QmygAAIABJREFUuZpMvaLJZiYcFwJerfLMJ7dCX7O2rupATQynHbzTlzscDjweD6Wlpf3KYykSgycQ4PW1a1lqt7Osvp6umEn41SUlUU/E3dPIJDgkJT9sWs06v4efFk2lJr8Uj8dDd3c3gUCAjIwM8vPzd3iwffx/fbxX56VmkYWj9kr+NBMlgjpnwjor7ITJVDg8rvhd2rzC5Xdq2aQAM47QxLBy4ZAvCwaDUaulyVRcWw/4gkHeWrdOMwluaKAzxiR4TlFRtIe4TxpEgz5wdXFt2waKDCaerrKSZTAgpcTlctHb20swGMRisZCXlxd92Pq4wcvDb/Wx+wwzPzsu+b6pSgR1jtvtxuFwUFZWNqme0Ds6OgiFQpSmaWgo5Xi74bP7YMU94OvW1s05URszLNtj0JdEog4TvoC5jvEHg7yzcWPUJLg1xiR4RoxJ8H46NQmWUnJJcyP1Pjc/LqzkjIKyftt6e3vp7e0FIDs7Wyva0Ce5+okuLBmC/7ugEIMhudelRFDnRMbHJpoj+M6IZMVWVFTo/ulX17g7YcXd2kT7QHg+4ILTYdGNMKW6365Syn5TJiZL+F2vBEMh3t+0STMJrq/vN+ewKi+PU8KCuHjaNF2ZBC93d/Pr1vXkG4w8U2Ul29A/ghUMBunt7cXlciGEICcnh9teCNDRK7n+tHymlST3YV+JoM7xer10dHRMuiSRSA94sol/wuhr0UKkXz4AQa82lcJ2rjbPsGBWdDe/3097ezsWi0WFonVESEo+jpgE19Wxubs7uq08J4eTq6upsdk4WAcmwVJKftayhm+8Li4srOD7BYPP/wsEAnR3d+PxeHj+MzNfbjJy+mILh++R3CiEEkGdE5nHNdn83wKBAK2trRQWFqralvGke7OWPLPqES2z1GCGb10E+/1Wc7WAaG3IyXbPpQtSSlZs2xb1RFw3iElwjc3GYbNmpcwk+DN3D5e3riM33BvMNQzdDr/fz1tf9LD0U9i1KsRF380adlpFvFEiqHMi6evFxcVDphhPVJqamsjOzqZgAlbZSDnOtdq0ironAQmmLNjjElh4NdJSolwm0gQpJV9GTILr6mjo6IhuK8zK4sQFC1JmEvzL5jV84e3jnIJyLigcvuJVsyPIdc90kZspueJoL0ajYadzDOOFEkGdE+kRTcZkhfaw6/dksZFKCR11WnHu1bXasjkH9v4F3m9dSkdfSM0dTCOklNtNgu12VsWYBOdlZHDCggUssVqTZhL8taeXn7esJVsYeLrKSoFxaBGWUnL5o0563JLrT80m29CH1+vFaDSSk5OTUDFUIqhzInPmJmNYsKurC5fLNalKxqWMls+1UmzrXtGWMwtx7/ZTumb/gCmVM9S4bBrS0N7OUrud2ro6Pm9ujq4vzc7mgwsuYF4SpsJc1bKOTz09nJlfyo+Khq969Zf/9LByrZ9zD8nmIFsWPp+Pnp4evF4vBoMhKobxTthSzvI6J/L0k24PHfHAbDYjpVSOEsmgfC84+WU480OY/l3wOrGsvI2yl/bD++EdSJ9ymkg3FpSUcM1BB/HZj3/M2p//nP93+OFUl5TQ5nLx4MqVSWnD+eEw6As9HXQG/cPuO69Se9BavU37vkfmrEaSAnt6emhpaYka+6YaJYJJYrKLIGjjoookMXURnPomnPo2VC7C4O0kd8UNyIfnwhcPQNC382ModEfEJPjB448H4Pn6+qT8plgzsznAko9Hhnimq3XYfedP1cKljU39H3ozMjIoLi6mrKwMi8WCy+WitbUVp9OZ0t8GJYJJYjKLoMlkQgihRDAVTD8UzvwATn6FQPHuGFxN8NYl8MgCWPWollmqSDsOmDaNspwc1jkcfN06vCjFi0hv8KXeDtoDQ3+Xq4qNZGcKOnpCdPTs2NMzmUwUFhZSVlZGdnY2brebtrY2Ojs78XrH5605FpQIJpHJZqwbQQiByWRSIpgqhIDZxyLOWYHjwL8RKJgP3RvgP+fDo7tB/T9BjtxEVZF6jAYDJy1YAMDzdntSzjk3w8J3sgvwSclT3S1D7mcwiGgB7cZtQz9kGY1GCgoKKC8vJy8vD5/PR0dHB21tbbhcrqT9VioRTCKTVQRBC4kqEUwtRpOZjN3OpPXoN/Ec+jcomA2OBnjlDHhiL1jzkuZzqEgLTgm7rL9QX5+0c/6goAIBvNzTSXNg6JD6UCHRwTAYDOTl5VFeXh4t7OB0OmlpadEMexM8bqhEMIlMdhEMhUK6GAifzOTk5JCVnYOj8jh8Z38FRzwIubtA21ew7CR4en/Y8IYSwzTgsFmzyM/M5KuWFtZ2diblnLMysjg0u5AAkie7hu4Nbk+OGfmDrxCC7OxsSktLo0k0vb29tLa24nA48PkSM46tRDCJTHYRBJUcowcKCwsxGo04unsJ7XYhXNgIh94H2WXQvByWHgnPHgpb3k91UxXDkGE0cvz8+UCSe4OFFRiA13o72eoffAxvRqmRDBM0O0N0u0Yfao9NosnJycHr9dLe3k5bWxt9fX38f/buPT7Ous77/+t7XXOeyUwmmRwKhba06QEQEQtsFRSFAhVoKSCIByqyP2/83dzrery7D2/XXVfXquzqcq8sIghV8YAslAqICKIWFKSAIPSQltLWQs6ZzEwmmckcvvcfk2uapDlnTkk+z8djHpnDlbm+nWbmPd9zNlu45nsJwRKazyFo7ZwhIVh+hmEQDAbJZrP09PTkVpk54+/gbw/AOV8DVxCO/A5+di789zpo3VnuIosxbFyZWzy9VP2CACfYnVzoDZIFfjBGbdBmDukXnEST6FisjXyHNpVGIhHa2toKNqpUQrCE5nMIGoaBzWaTuYIVwm634/f7SSQS+S1xsHvh7M3wt6/Dmi+BowoOPgr3nAkPXgGdr5S30OIYFy9bhstm449HjtAyZHeKYrsu0IAJPB4PcziVGPUYq0l03xSaRMcysqnU7XbnR5V2dnbS1zf9+a8SgiU0n0MQZHBMpfF6vbjdbqLR6PD+FmcA3vFPcMMBWP05sLlh/wOw9TR4+EMQ3le2MovhfA4HFy5dCsC2EjaJLrA7WeerIQts7Rm9NmgNjmmeQU1wNA6Hg+rqahoaGggEAkdbNKZJQrCEJATtpNPpgrbni5kJBALYbDbC4fCx/y+eELz7G3DDa3D6TWDYYM+P4a5V8Ku/hejh8hRaDHPFYJNoKfsFAT4SaMCO4sm+Hl4f6D/m8SUNNkwDjnRm6EsW/j1vLcFWX19P7QyWjpMQLKH5HoJWv6A0iVaOof2D4XB49L9P3wI4///mBtCcekPuvlfuhO83wRP/C3pbSltoMcxlK1ZgKsWTBw8S7j82jIql3ubg0qoaNHDXKH2DDpticb0NDbzWWtz3/Ey2CpMQLKH5HoIyQrQy2e12AoEAyWTyaP/gaPyL4KI74PrdsPKDkEnBn/8T7lwKv/s89HeN/buiaGrcbs5bvJh0NstDzc0lPfeH/A04lGJHX4Tm5LH9cssXDDaJjjNpvtwkBEtovoegaZqYpikhWIE8Hg9utzu/2v+4gk1wyT2w6WVougLS/bDzm3DHEnj6S5CMlKbQIi8/SrTETaK1NjuXV+W2SLsr0nrM403HTbxyTLlJCJbQfA9BQJZPq2DV1dX5/sFJNVmHToX1/w0f3gmLL4aBGDzz5VwYPrsFUvHiF1oAcPlgCP5q/37iRZpUPpZr/fW4lMEz/TF2JYf/ny9ttKEUHOxIk0xV5mefhGAJSQgeHRwz31+HSqSUoqamBoDu7u7Jr+7T8Ha48pdwzQ5Y+G5IhOGpf4A7ToIX/gPSow+hF4VzvN/P2ccfT386za9ee62k5642bVxh1QZ7htcGPU6DE0ImmSwcaKvM2qCEYAnN550kLLK3YGWz2WzU1NSQyWTo7u6e2t/qwnPg6ifhql9D41nQ1w5P/j3c2QQv357rQxRFU461RC3X+OvwKoOdiV5eTgzvV25aMPNJ88UkIVhCEoIyOGY2cDgcBINBUqnU2CNGx6IULLoAPvgMXL4d6k6D3iPw6/8Bd62EXT+ErKwfWwxWv+Av9u5loMRr9PpNG1f56wD4fk/rsL+Z5QWcNF8MEoIlJCGYq2kYhiEhWOFcLheBQIBEIkEkMo2BLkrB0svgIy/CpT+D4AqIHIBfXgdb3wLN98n2TQXWVFvLqfX1RJJJfnvwYMnPf5W/jirD5KVknBeG1AatwTEH2tKkM5X32SchWEISgjkyOGZ28Hq9VFVV0dfXR2y6S3IpA1ZcDR99BS6+G/yLoXs3/OL98KPVcOBh2bGigMqxlqjFZ5hcPUptsMptsCBoMJCGQx2V1yQqIVhCEoI51vJp8/11mA2qqqrweDzEYrEZrc+IYYNTNsHH9sL5t4LvOGh/ER64FH7yTjj8m8IVeh6z+gW37dlDpgwrM11ZFSJgmOwa6OPZxNEvTke3VpIQnNckBHOswTGyt+DsEAgEcDqdRCKRiecQTsR0wOmfgI/th/P+Hdx10PJH+Pn5ucubfyxMoeeptzY0sLi6mrZ4nGeOHCn5+d2GyQcD9UBupKj1Wbe8gucLSgiWkIRgjsPhAJj5B6ooCaUUwWAQm81Gd3d3YZqy7W54+6dy2ze98yu5RbsP/wZ+8o5c7bDtxZmfYx5SSpVtLVHLel+IGtNG80A/T/dHgaMjRPe3pslmK+vzT0KwhCQEc2w2GzabjURC5o/NFoZhUFNTg2EYdHV1Fa5P1+GDv/lCbvums7+Q287pwMPwozNy/YZdpe/bmu02DjaJ3r97d1k+a1yGwYf8R2uDWa2pqTKprTLoH9Ac6a6sFiAJwRKSEDzK5XIxMDAgr8UsYpomtbW1KKXo6uoavv3STLmCcM5XcmH49s/kNvptvg+2npobUdpzoHDnmuPWLFxIg9fL6z09vNw2+jZHxXZpVS11pp0DqQS/68uNLq7UJlEJwRKSEDzK6XSitZYm0VnGZrMRCoUwDIPu7u7CBiGApw7OuznXZ/jWT4Ayc3ML71qRm2sYK30/12xjGgYbVqwAyjNKFMChDD4caADg7p5WMloPGRxTWSPDJQRLSELwKIfDgWEY0iQ6C1k1QqtptOBBCFB1PFxwa2406SnX5+YUvnw73LkstwpNvDw1nNminKvHWNb5gjSaDg6nkzwRDx+tCbZU1rKJEoIlJCF4lFIKp9MpNcFZygpC0zTp6uoq3v9jYAlc/H346C5YcQ1kkrn1SO84CXb8A/R3F+e8s9x7liwh4HTyl/Z29neX5zWyK4NN1bna4NZIGzV+RcCjiPVrWnsqZ6EECcESkhAczul0kslkZOL8LGUFoTVqtKhfaGpWwKU/hetegqXrId0Hf9qS27Hij1+GZLR4556FHKbJpcuXA/BAmZpEAdZ6g5xgc/JmeoDH4mGWWUuotVTOe76oIaiUulgptVcptV8ptXmMY65WSu1SSr2qlPpxMctTCWQniaNcLheANInOYiODsOj/l3WnweUPwgefhUVrYSAKf/hSrmb43DchNYMJ/XNMufYYHMpUiusGa4M/jLSxdIEJVNbgmKKFoFLKBL4DrANOBq5VSp084pgm4B+Ad2qtTwH+vljlqRQSgkcZhoHD4ZAQnOUMw8gHYTgcpr+/v/gnXXAWXPUYXP1bOP4cSHTB7z+f2+X+xf+EtDSzX7xsGS6bjWeOHOHN6S57VwDv8VSzyO6kLZOitTq332AlrRxTzJrgWcB+rfUBrfUA8FNgw4hj/j/gO1rrMIDWur2I5akIEoLDuVwuUqmUrB4zy1lBaLfbCYfD9Pb2TvxLhXDCu+Ga38OVj+b2NYy3wm/+F3x/Ofzl+5CtnA/bUvM6HFy0dCmQW0atXEyluD7QCMB21Y7boejuzdIVq4z3fDFD8Hjgr0NuHxm8b6jlwHKl1NNKqWeUUhcXsTwVQUJwOKfTCcjqMXOBFYRut5toNEpPT09p/taVgsUXwYeeg/UP5Ha8jx2Gx26Au0+G3T+ZtztWVMIoUYBzPQGW2l10ZlP46nL/F5VSGyz3wBgb0AScB1wLfE8pVT3yIKXUx5VSO5VSO0tcvoKTEBzObrdjmqY0ic4R1hJr1u4T3d3dZEu1kLNS0HQ5fOTP8L4fQ7AJwvvgkQ/CD94K+7bNux0rLl2+HFMpnnz9dbpL0Uw9BkMprq/O1QYPBnJNs5XSL1jMEHwDOGHI7YWD9w11BNiutU5prV8HmsmF4jBa69u11qu11quLVtoSkRA8lsvlIplMyusyh1RVVVFdXc3AwACdnZ2k0yX8wDNMWHVtblrFhXdC1YnQ+Qps3wj3nAWvPzpvwrDG7eY9S5aQ0ZqHmpvLWpZ3uP2sdLjprs6FcXOFjBAtZgg+BzQppZYopRzAB4DtI47ZRq4WiFIqRK55dE6vjyQheCxr9ZiiTLoWZePxeKitrSWbzdLZ2Vn6/1/DBm/5GHysGd77f8HbCG074f518LN3wZHfl7Y8ZVLOPQaHUoO1wWRgAG1maevJEu0rfzN10UJQa50GbgJ+BewG7tVav6qU+rJSav3gYb8CupRSu4Angc9prbuKVaZKICF4LKfTiVJKmkTnIIfDkV9mraurqzQjR0eyOeFtN8ENr8G7vgGuWnjjKfjZu+G+C6HlT6UvUwldPhiCv3rtNeJl/qJ5pquKU90e+oO5cuxrKX+TaFH7BLXWj2itl2utl2qtvzp43z9qrbcPXtda609rrU/WWr9Fa/3TYpanEkgIHktWj5nbrPVGHQ4H4XCYaDRanveA3QNnfi63fdM7vgwOPxz6Nfz4bNi2ATpeLn2ZSuC4qir+ZuFCEuk0j+7fX9ayKKX4WPUC+mty7/VX3yh/68+kQ1ApdbxS6h1KqXdZl2IWbK6SEBydy+UinU6Xtu9IlIy1FZPX66W3t5eurq7yTYtx+mHNF3M7Vpy1GWweeG17bvDMQx+A7r3lKVcRlXuPwaHe5vKxsCEXPc8fKX/rz6RCUCn1deBp4P8Anxu8fLaI5ZqzJARHZ02VkCbRuUspRSAQIBgMkkql6OjoKO//t7sGzv1armZ4xt+D6YS9P8tNq3j0eogcLF/ZCszaY/Ch5mYGKmBO7vVLatGGJt6jaOkr7wCZydYELwdWaK3fp7W+bPCyfsLfEseQEBydaZrY7XYJwXnA7XZTV1eHaZp0d3eXr3nU4m2A93wLPrYPTvs4KANevTs34f7x/x963yxf2QpkWU0Nb6mvJ5JM8uTrr5e7OLzN58MezKBQ/PBAeYeBTDYEDwD2YhZkvrBCUILwWNZGuyWbVybKxuonrIjmUYv/BFj7Xbh+D5z8kdxqMy/9V24ptt9+Bvo6ylu+GaqUUaKWMxbmWn9eeCNJd6Z8tcHJhmAf8Gel1HeVUrdYl2IWbK6SnSTGJqvHzC8V1zxqqV4K634AH30Fll8F6QQ8/++5HSue+j+Q6Cl3CafFWj3mwb17yVTAF801J7gBsHc5+EmkfCtmTjYEtwP/AvwBeH7IRUyRhODYZKPd+Wlk82gkEqmM90ftyXDZz+HDL8BJl0AqDs9+NReGz3wVBkq0PmqBnNbQwJLqatricf545Ei5i8PSRjtKgTPiYHu4m450eWqDkwpBrfVW4CccDb8fD94npkhCcHyyesz8NLR5NB6P09HRUTktAg1vg40PwbV/gBPfC8keePr/5LZvev5bkCrfcmRToZQ6upZoBTSJuh2KE0MmSivMHjv3RNrKUo7Jjg49D9hHbmukW4FmmSIxPRKC43M6nWSzWdlodx6ymkdDoRAAXV1dlVMrBDhuDbz/idxlwRro74Dffhq+vwxeug0y5Z/zNpGhewxWwuvadJwNAHe3k4d7u2lNl/41nGxz6L8BF2qt3621fhdwEfCt4hVr7pIQHJ+sHiMcDgd1dXX4fD7i8Tjt7e2VUyuEXG3w2qdh48NQ/7bc6NHHPwF3rYRXt1b09k1rTjiBRp+Pgz09vNRWnprXUMsHd5pf0OMljeZHZagNTjYE7Vrr/AxSrXUzMlp0WiQExycb7QrIvU/8fj+hUAilVL5WWDEjh5WCk94HH94Jl90HNasg8jo8+lG4+1TYe29Fbt9kKMWGFSuAyhglumxBriaY6bZhZOGXvd28kSrtF57JhuBOpdQdSqnzBi/fA2b9tkblICE4MWv1GGkSFSNrhRUzgtSiDFh+JWz6S25EaeAkCO+Fh66BH54Br/2i4nasqJQ9BgGq3AYLgibpDJybrCULbC1xbXCyIfgJYBfwd4OXXYP3iSmSEJyY2+1GKUVfX1+5iyIqwMhaYXd3N11dXZW1xJ5h5uYWXr8nN9fQtxA6XoJt6+Ena+DQ4xUThuctXkzA6eSV9nb2dZV/v4Llg/2Cq2IBTOCJeJjDqdJ90Zns6NCk1vrftdZXDF6+pbWuoEb62UNCcGKGYeB2u+nr66uc5i9Rdlat0O/35+cVln21mZFMe27VmRv2wXv+Azz10PIs3LcW7n0PvPF0uUuIwzS5bLBJtBJqg9bgmNY2eJ8vVxu8u6d0tcFxQ1Apde/gz78opV4eeSlNEecWCcHJ8Xq9aK2lNiiGUUrh8/moq6vD7XbT29tLe3t7ebZoGo/NBWf8XW5d0nO+Bq4gHPkd/PQc+O910FbeadaVtHpM0+DgmP0tKT5YVYcdxZN9PRwYKM3/6UQ1wU8O/rwUuGyUi5giCcHJsdvtOBwOCUExKtM0qa6uzu9VGA6H6erqqrx+ZLsXzt6c27FizZfAUQUHH4UfrYbtV+Z2vC+Di5YuxW2z8ewbb/BGNFqWMlhqfAZ1foNECpIRk0uraoHS1QbHDUGtdcvg1U7gr1rrQ4ATeCsw+1eVLQMJwcnzer2k0+nKGgghKoq1aW8gEMg3kfb09JR/HdKRnAF4xz/BDQdg9efA5oZ998PW0+DhD0F4X0mL43U4uGjZMgC2VUCTqDVKdN+baT7kr8ehFDv6IzQni/8leLIDY34PuJRSxwOPAR8B7i5WoeYypVQ+CMX4XC4XpmkSj8fLXRRRwZRSeL1e6uvr8Xq99Pf3097eTjQarbw+ZU8I3v2N3C73p98Ehg32/BjuWgW/+luIHi5ZUSppj8Hlx+WaRPe1pKi12bm8Krdgwl2R1qKfe7IhqLTWfcAVwK1a6/cDpxSvWHObbKc0OUopPB4PyWSyskYCiopkGAaBQID6+vph/YW9vb2V937zLYDz/29uAM2pN+Tue+VO+H4TPPG/oLdl/N8vgEuXL8dmGPz24EG6ytztYI0QbX4zjdaaa/31uJTBM/0xXk0W90vwpENQKbUG+BDw8OB9ZnGKNPdJCE6ex+NBKSW1QTFpVn9hXV0dDoeDaDRKe3s78Xi88t53/kVw0R1w/W5Y+UHIpODP/5nbvul3n4f+4k1hCLrdvGfxYjJa81Bzc9HOMxl1foOAR9Gb0LSGs1SbNq60aoM9xa0NTjYE/x74B+ABrfWrSqmTgCeLV6y5TUJw8kzTxOVy0d/fX3lNW6Ki2e12ampqCIVCmKZJJBKho6ODvr6+ynv/BZvgkntg08uwbCOk+2HnN3M7VvzhnyAZKcpph64lWk5KqXyTaHNLbnDT1f46vMrg+UQvLyWKt2PHZOcJ/k5rvV5r/fXB2we01n9XtFLNcRKCU+P1eslms5U3DF7MCtbgmZqaGpRS9PT0VG7NMHQqbLgfPvQcLL4YBmLwx3/OheGzW3LbORXQ5StXooDHXnuN3oHyLgDeNKRJFMBv2ni/vw6A7/e0Fu3/aqJ5gt8e/PkLpdT2kZeilGgekBCcGofDgcPhkCZRMSMul4u6ujpqa2vzNcO2tjZ6e3srr5WhcTVc+Uu4ZgcsfDckwvDUP8AdS+GFW3Ib/RbAgqoq/mbhQhLpNI/u31+Q55yupiEjRK3Pxyv9dVQZJi8n47xQpNrgRDXBHw7+vJncThIjL2IaJASnzpouUVG7CYhZyel0EgqFCIVC2O32fJ9hLBarvDBceA5c/SRc9WtoPAv62uDJT8KdTfDy93J9iDNUKWuJHldj4nEqwvEsXbHc/4PPMLmmyLXBieYJWssa7AR2DDaL/g54Cniu4KWZJyQEp87lcmEYhtQGRcE4HA5qa2vzA2hisRhtbW1EIpHKGo2sFCy6AD74DGx4EOpOg94j8OuPw92rYNePIDv9eZFWv+BDzc0MlHF+paFUvjZoNYkCXFEVotqwsWugj2cTscKfd5LHPQF4htx2A48XvDTzhITg1FlzwRKJRGV9QIlZzxpAYy3F1tfXR3t7O11dXZXV8qAULFsPH3kRLvkpBFdAz2vwy4/AD06D5v+e1vZNS2tqOK2hgWgyyW9ef70IBZ88a6rEvpaj73G3YXJtoHi1wcmGoEtrnW+QHbzuGed4MQ4JwemxpkvIUmqiGOx2O9XV1TQ0NFBVVUU6naarq6vyBtEoA1ZeAx99BS6+G/yLoWsX/OKq3HJsBx6Z8o4VlbKWqLWOaPObw5t51/tC1Jo29g3081R/YZd5m2wIxpVSZ1g3lFJvB2So3jRJCE6PNV2iIoe4iznDMAyqqqqor68nGAyilMoPoolGo5XTEmHY4JRN8LG9cP6t4DsO2l+EBy7JLdR9ePKz2Kx+wQf37iVTxn7RE+tMnDZoj2SJ9B0th8sw+JC/AcjNG8wW8P0/lXmCP1dK7VBKPQX8DLipYKWYZyQEp8+aLiG1QVFsSincbjd1dXWEQiGcTifxeDzfVNrf318Z72PTAad/Aj62H877d3DXwZt/gJ+/F35+Abz5zIRP8Zb6ek4KBmmPx/nDX/9agkKPzjQUSxuPjhId6pKqGupNO6+nEvy2r6dg55zsPMHngJXkNtK9EVg1ZNCMmCIJwelzOBzY7XYJQVFSDoeDYDBIfX09fr+fTCZDOByurNqh3Q1v/1Ru+6Z3fiW3aPfhJ3Kb+j5wKbT/ecxfVUpVzFqiTSMmzVscyuDDgVxtcGtPG5kCfYZOKgSVUh7gfwOf1Fq/AixWSl1akBI7PqUFAAAgAElEQVTMQ1YIShBOj8/nI5VKSRCKkjNNE5/PR319PbW1tcfUDiuiqd7hg7/5Qm77prO/kNvO6cDD8MO3wS+uhq7R+/02DjaJ3r97d1n/DfnBMW8e+8Vina+GBTYHh9NJnoiHC3K+yTaH3gUMAGsGb78BfKUgJZiHZDulmXG73fkh7RU3r0vMG06n85jaYU9PD62trfT09JBMJsv7HncF4Zyv5MLw7Z8G0wnNP4etp8IvN0HPgWGH/83ChTT6fByKRPhza/F3bxjLknobNgPe6MoQTwx/f9uU4jqrNhhpI12A13eyIbhUa/0NIAUwuKOE7Ac0TRKCM2d96PT2Fm9NQSEmY2jtMBQK4Xa7SSQS+ZGl0Wi0vJv9eurgvH/Lbd/01k+AMmHXD+CuFfDrGyF2BMjN07t8xQqgvKNE7TbFkgYbGtjfemxtcK03yAk2J2+mB/hVb/eMzzfZEBxQSrkBDaCUWgpU0ASa2UVCcOYcDgcej4d4PF4Z/TFCkPu7tKZZBINB7HY78Xicjo4OOjo66O3tLd+Gv1XHwwW35kaTnvLR3JzCl78Ldy6DJz8F8baKWT1m5DqiQ5lKsak6Vxv8YaSNgWnMjRxqsiH4JeBR4ASl1D3kJs9/fkZnnsckBAujqqoKgGi0sPOGhJgpa2RpTU0NDQ0NBAIBlFJEo1Ha2tro7OwsXyAGlsDFd8FHd8GKayCThBe+DXecxHlH7qTa6eTVjg6au0bfxumWW25h1apVXHnllaxZswan08nNN988pSJ0d3ezdu1ampqaWLt2LeHw8P49a+WY5146wIUXXsiqVas4+eSTOXjwIADv8VSz2O6iLZPikRnWBicMQZX7xN5DbkPdjwI/AVZrrX87ozPPYxKChWGaJlVVVSQSicpa2UOIIQzDwOv1EgqF8v2HWuvyB2LNCrj0p3DdS7B0PaT7sO/8GpfpFwF44JXRR5Peeuut/PrXv+a//uu/uOWWW/jsZz875VNv2bKF888/n3379nH++eezZcuWYY8va7SjFPzoWzfyyU99lt27d/OnP/2J+vp6INd0+9HBvsF7Im0kZzA2YMIQ1LlP6ke01l1a64e11g9prTunfUaRJyE4c16vF5vNRjQalddTVDybzYbP56Ourm7UQOzo6CAWi5W2D7HuNLj8Qfjgs7BoLRuNlwG4/6n74LmbIXV0FPaNN97IgQMHWLduHffccw9nnnkmdrt9yqd88MEH2bRpEwCbNm1i27Ztwx53ORSuxD6ymTRLTn0PkBsV7vEcXajsXE+AZXYXnZk0v+id/ubDk20OfUEpdea0zyKGkZpg4Sil8Pv9MmVCzDqjBaJSilgsRkdHR34x75KNMl1wFlz1GBdd/R3cKsOf0o0c+e2/5PoMX/wOpJPcdtttHHfccTz55JN86lOfGvOpzj33XE4//fRjLo8/nltyuq2tjQULFgDQ2NhIW1vbMc/hTBzE4Qlww3VX8ba3vY3Pfe5zw2rLhlJcX90IwI8j7dP+Z9smedzZwIeVUgeBOLmRoVprfdq0zzyPSQgWlsvlwul0EovFcLvdGMZkv9sJURmsQPT5fGQyGZLJJIlEgr6+PuLxOIZh4HQ683/rxfwb9yx5Lxev6OCBPXvY5lnLTfH/ht/cBM99A9Z8aVLPsWPHjkmfTymV/0wcqt6vad3/Rz74P3fwlb89hWuuuYa7776bG264IX/MGreflQ4Pewam/wV4sq/kRcBJwHuBy4BLB3+KaZAQLDy/3082myUWK/xWK0KUkmmaeDweampqaGxspKamBpfLRTKZJBwO09raSmdnZ1GbTfOjRH2XwfoHcjvexw7DYzdA7xsT7lgxUU2woaGBlpYWAFpaWvJ9fUO9/dQTqV34FiLqBLQyufzyy3nhhReGHaOU4vrBkaLTNW5NUCnlIrdM2jLgL8CdWmsZjz5DEoKFZ7fb8Xq99PX14fF4ptVPIUSlUUrhcrlwuVxorUmlUvlaYiwWIxaLYRhGvoZYqFriJU1N2AyD3x06RNf7P0vtRy6DvffCH78E2X3w+I2w7z/hHf8y6o4VE9UE169fz9atW9m8eTNbt25lw4YNxxzz7nPORg9EiPV0crC9it/85jesXr36mOPOdFXx1brFvHOa/1Y13gexUupn5CbI7wDWAYe01p+c5rkKQqnjtNZvlrMIM5bNZmltbcXv9+Pz+cpdnDkjm83S3t6O3W6ntra23MURoqiy2Wx+ZHQymcyvnmS32/OB6HA4Rm1qnIyLfvQjHnvtNe7asIGPnn764EnTLD6hkZ2fcZGOvMHq/4Bo0sCwu/H5q9m1axd+v3/C5+7q6uLqq6/m8OHDLFq0iHvvvZeamhp27tzJbbfdxh133AHA5n/7Bbf/+2b8bsV556zm9ttvx+FwjPqcSqnntdbHpuQEJgrBv2it3zJ43Qb8SWt9xpi/UAJzIQS11rS0tFBVVZWf6yYKIx6PE4lE8k1IQswHQ2uJAwMDDAwMoLVGKYXD4cDhcOB0OrHb7ZMOxdt27uQTDz/MZcuXs/3aa4c/mE7CX74Hz34V4oNLrB1/bm6ZtoXvKti/60/7knzv13FOPdHOJy8d/7NyuiE4Ub053+AszaCFY3UES3No4Xk8Hmw2G5FIRNYVFfOGFXZVVVXU1tbS2NhIbW1tfuuxWCxGZ2cnra2tdHV1EYvF8kE5lg0rVqCAx157jd6BgeEP2pzwtptyS7G96xvgqoU3dsDP3g33XQQtfyrIv8vaZHd/S4pMtjiflxOF4FuVUtHBSww4zbqulJJlOmZAQrA4lFJUV1eTzWbp6SncnmNCzCZKKZxOJ36/n7q6OhobGwkGg3g8njFDceRUjAVVVaw54QSSmQy/3Ldv9BPZPXDm53LbN73jn8Hhh0OPwY/Phm0boOPlGf07gj6DOr9BIgVHOouzmMC4Iai1NrXW/sFLldbaNuT6xA2/YkwSgsVjfSNOJBLE4/FyF0eIsjMMA7fbTSAQyIdiTU3NsFDs6urKjzyNRCL09/fnF9SecC1Rpx/W/GNux4qzNoPNA69thx+cDg9dC917p112a2ul5pbiNEbKhKoykRAsLp/Ph8vlIhqNMjCyKUeIec4aUToyFL1eLwB9fX2Ew2HOGRxg9ou9e+mOREilUuN/brlr4Nyv5WqGZ3wSTDvs/SncfTI8+jGIHJxyWfOb7L5ZnOkgEoJlIiFYfNXV1RiGQTgclv5BIcZhhaLf7ycUCtHY2EgoFOItCxdyaihEbyrFI3v20NHRka8tRqNR+vv7R1/z1NsA7/k2fGw/nPZxUAa8ehd8fzk8/j+hd/KDG63FtPe3pIvymSkhWCYSgsVnGAbBYFD6B4WYImugjc/n46pTTgHgt62tBIPBfG0xHo8TDodpa2ujra2N7u5uYrEYiUTiaDD6T4C134Xr98DJH4FsGl66Fe5cCr/9LPR1TFiWOr9BtVfRm9C0hAv/ZVZCsEwkBEtjaP+gbMArxNRZq8dsb27GMTjYxqot1tXVEQgEcDqdpNNpYrEY3d3dxwaj63gyF94FH30Fll8F6QQ8/29wx0nw9BchMfaXVKVUfpRoMZpEJQTLREKwdKz+QWtYuBBi8k6tr2dpMEhHXx9P//Wv+fuVUvmVmqqrq6mvr883owYCARwOx7HBmK6l6x230bvx92QWXQypXnjmK3DHEnj2X2Fg9C+q1uCYfUUYHCMhWCYSgqUl/YNCTI9S6uhaort3j3usYRg4HA68Xi/BYDAfjLW1tfkaYzabJeZeRts77qTjgm0kG86BZA889QX095aQfvabZAeGj+o+utP8BANzpkFCsEwkBEtL+geFmL6NK1cCcP+ePVP+3LJ2wLBqjNZo1Lq6OrxNa0lc9hCRC+9nILQalejE9tTn0XcsI7rjG3R3tBKNRql2DeB1Knrims5oYb/ESgiWiYRg6TkcDvx+v/QPCjFFZy9cyAKfj8ORCC+2ts74+aymVI/HQyAQIPCWjTiu+xOZDdvJhk7H7G/F/6f/TeD+s8n+5S4i3V2cEMw1hT6/t5twOEwsFqO/v3/iaRsTkBAsEwnB8vB6vbjdbqLRqGzCK8QkGUpxuVUbnKBJdNqUwlx2GcZ1z8Nl90HNKszew1Q/8/cseGwtJ7ubATjUZZJKpYjFYoTD4fy0jemSECwTCcHyqa6uxul00tPTQ39/f7mLI8SskO8XnGj1mJlSBiy/Ejb9Bdb9AAInocJ7WbX7JgAOt/VTX1fHggULqKurGzZtYzokBMtE9hQsH6UUNTU1OBwOenp6SCQS5S6SEBXv3YsWEXS52NXRwd7OzuKf0DBzcwuv3wNrv8sJnjBO3Ut7n5ueey5BHX4Cu82G2+2e1PZNY56mgEUWU2CFoIxULA8rCG02G+FwmGQyWe4iCVHR7KbJZZNdS7SQTDuc9nHMv93DsuoIAPs6nXDfWrjvAkiEZ/T0EoJlYrPlhvyOuuSQKAnDMKitrcVms9Hd3S1zCIWYwMZi9wuOx+aiaeUyAJqP/zQ4A3D4N7nLDEgIlokVgqlUcRaFFZNjGAY1NTWYpkl3d7f8fwgxjguXLsVjt/Pcm2/y10ik5Oe31hHdlzkNFq/L3ZmZWSuOhGCZmKaJYRjyoVsBTNOktrYWpRRdXV2k07J/tBCj8djtXLwsVxvbVsom0UFL6m3YTHijO0Oc6tydmZm14BQ1BJVSFyul9iql9iulNo9z3JVKKa2UWl3M8lQau90uH7gVQoJQiMm5YrBJtKT9goPsNsVJDYO1wYGm3J3ZmVUkihaCSikT+A6wDjgZuFYpdfIox1UBnwSeLVZZKpXdbp/xRE9RODabjZqaGrTWdHV1SS1diFFcsnw5NsPgd4cO0VmGubb5JtHk0twdlRqCwFnAfq31Aa31APBTYMMox/0L8HVg3o1Tt9lsaK1lcEwFsdvt1A5uJNrZ2SnTJ4QYodrl4vwlS8hqzS/2Tn/H+Olabm2y239S7o4KDsHjgb8OuX1k8L48pdQZwAla64eLWI6KZbfn/jOlxlFZ7HY7oVAIu91Od3e3LLEmxAhD1xIttZMabRgKDicWkMBb2X2C41FKGcC/A5+ZxLEfV0rtVErtLH7JSsdms6GUkv6nCmT1EVpLrPX09EiztRCDNqxciQJ+/dprxEo8x9ZlV5xYZ5LF4IBxZkXXBN8AThhye+HgfZYq4FTgt0qpg8DfANtHGxyjtb5da71aaz2nBs4opbDZbFITrFBKKYLBID6fj76+Prq7u2VxAyGARp+Pd5xwAslMhl/u31/y8y+3Ntk11kCmckPwOaBJKbVEKeUAPgBstx7UWke01iGt9WKt9WLgGWC91npO1fYmIiFY+fx+P9XV1QwMDNDZ2Sk1dyEo4Vqio7D2F9xnrKncmqDWOg3cBPwK2A3cq7V+VSn1ZaXU+mKdd7ax2+1kMhmpYVQ4j8dDTU0N2WyWzs5OWWZNzHtWv+DDzc0kS/zF0BohesB4O6nUzAYWFrVPUGv9iNZ6udZ6qdb6q4P3/aPWevsox54332qBcHTlGKldVD6n00koFMIwDLq6uohGo9JPKOatJcEgpzc2EhsY4InXXy/pub0ug+Pd3aSVi9f76mb0XLJiTJnJCNHZxWazEQqF8Hg89Pb2SvOomNfKuZbocn8HAPviC2f0PBKCZSbLp80+hmFQXV1NTU0NmUyGjo4O4vF4uYslRMlZ/YIP7t1LpsRdOk3VXQA09504o+eREKwAsnza7ORyuairq8PhcBCJRGT0qJh3TqmrY1lNDZ19fTx1+HBJz900uK3Sa/0nkslOv1tCQrACWCNEpX9p9rHmEwYCAZLJJO3t7bLKjJg3lFJlW0u02p2lPvsaSe3ir53THxwjIVgB7Ha7LJ82y3m9XkKhUH5Lpp6eHvn/FPPCxiFTJUr6Rd6w05T9IwDNb06/O0lCsALI4Ji5wVpuzefz0d/fn+8rlBq+mMvOOv54jquq4nAkwgstLaU7sWFn+WAI7muZfneShGAFkOXT5g6lFH6/n7q6Oux2O5FIROYVijnNUIrLV6wASjxK1HTQlP0DAPvelBCc1ZRSmKYpNcE5xGazUVtbm59g39XVRTgcliZSMSeVZfUYw05IHyZodBFPysCYWc/aW1DMLS6Xi/r6eqqqqkgkErS3t9Pb2ytNpGJOedeiRQRdLnZ3drKns7M0JzXsKKDJvmtmT1OY0oiZkuXT5i6lFFVVVdTV1eF0OolGo7S3t0t/oZgz7KbJ+sEm0QdK1SRq5sZSNJmvzOhpJAQrhCyfNvdZO9fX1tZimiaRSETCUMwZJd9j0HAAsMJ4kaUNtuk/TaHKI2ZGRojOH9YapCPDsK+vT8JQzFoXLl2Kx25n55tvcjgSKf4JB2uCC9QBNl/pn/bTSAhWCFk+bf4ZGYY9PT10dHRIGIpZyW23s27ZMgC2laI2aORCsGK3UhJTJ8unzU9WGNbU1KCUoqenh/b2dmKxmPQRi1mlpKNErRCc4aa6029IFQVns9no6+srdzFEmbhcLlwuF4lEgng8TiwWo7e3F7fbjdfrzTeZC1GpLmlqwm4Y/P7QITriceq83uKdzMz1CZIdmNHTSE2wgljLp0ltcH5zuVzU1tZSX1+Px+PJrz7T1dUl65KKihZwuTj/pJPIas0vmpuLe7IC1QQlBCuIDI4RQ9lsNgKBAA0NDfj9ftLpNN3d3fm5hjLxXlSiku0xKH2Cc49MkxCjMQwDn89HfX09wWAQwzCIRqO0tbXR3d1Nf3+/DKQRFWPDihUo4NcHDhAr5nKBEoJzj1Iqv62SECMppXC73YRCofwqNKlUinA4TFtbG5FIRP52RNk1+Hy888QTGchkeGTfvuKdyOoTzEif4Jwiy6eJybDZbFRVVVFfX09tbS1Op5O+vj46Ojro6Oigt7dXWhRE2WwsxR6DUhOcm2T5NDEVSimcTifBYJCGhgYCgQBKqfzSbBKIohysEHx43z4SxfrbMyUE5yTpFxTTZRhGfnNfazCNBKIohyXBIKc3NtI7MMATBw4U5yTKzP3UWcjKzvJzhowQFYVgmiY+n2/cQIxGowwMDMigGlEUVxR7lKhSQ+YKys7yc4a1fJp8WxeFMjQQ6+vrCQQCGIZBPB6ns7OTtrY2wuEw/f390gwvCmbj4Oox25ubSRfr76oA/YKyYkwFksExolhsNhs2mw2v10s2myWZTJJMJkkkEvT396OUwm6343Q6cTqd2O12lFLlLraYhU6pq6OppoZ93d08dfgw5y1eXPiTFGDCvNQEK5BMkxClYBgGbreb6upqGhoaCIVC+Hw+AGKxGJ2dnbS2ttLd3U08Hpe/STElSqmjo0SL1SRagJqghGAFkuXTRKkppXA4HFRVVREKhWhsbKSmpgaPx0M6nSYSidDR0ZFvOpVQFJMxdEHtovQ9F2CuoDSHViBrcEw6nc6PFhWilAzDyC/oDZDJZPJNpwMDA/T39+ePczgc+Ys0n4qhzjz+eI6rquKv0SjPt7Sw+rjjCnsC6ROcm6zgS6VS+Q8hIcrJNE08Hg8ejwfIfUEbGBjIX6yFva0+RSsQHQ4HpmmWs+iijIzBJtHvPPcc9+/eXfgQLMBcQWkOrUCyfJqodDabDY/HQ3V1NfX19cOaTwHi8Xh+OTdrjdPe3l6SyaSMQJ1nirp6jPQJzl2ywa6YTazm00AgkO9TDIVCBAIBHA4H6XSaaDRKV1cXra2ttLe3Ew6HJRhnoVtuuYVVq1Zx5ZVXsmbNGpxOJzfffPOYx79r0SJq3G72dHayu6MDgO7ubtauXUtTUxNr164lHA6P+fvRaJSFCxdy0003HfugIX2Cc5bD4aC/v59UKiWbqYpZxxpo43A48A5urJrNZkmlUsMuVt8i5Jpc7XY7drsdm82G3W7HNE3pY6wwt956K48//jgOh4NDhw6xbdu2cY+3myaXLV/O1pde4oE9e1hVV8eWLVs4//zz2bx5M1u2bGHLli18/etfH/X3v/jFL/Kud71r9CeX5tC5y+12o5QiHo+XuyhCFIRhGDidTnw+H8FgMN+MWltbi9/vz9cYY7EY4XCY9vZ2Wltb6ejooKenJ19rlH0Uy+fGG2/kwIEDrFu3jnvuuYczzzxzUl/SrVGi1uoxDz74IJs2bQJg06ZNYwbp888/T1tbGxdeeOHoT1yAeYJSE6xQ1hyu/v5+/H4/hiHfV8TcYwWj0+nM32dND0qlUvmfiURiWJOpYRj5if/Wxao5iuK57bbbePTRR3nyyScJhUJjHnfuuecSi8Xyt7NaY7S38/zatRy+5hra2tpYsGABAI2NjbS1tR3zHNlsls985jP86Ec/4vHHHx/9RDI6dG7zer309fXR39+fb1ISYq6zRpiOrGFkMhnS6fSwy8hwtAaVDb2YponNZpMvkiW0Y8eOY+57/89/zn27dh0zcV4pNWqT96233sr73vc+Fi5cOPaJ8muHSp/gnGQNMY/H4xKCYt4zTRPTNIfVGiFXYxhac8xkMsf0N8Lw2qMVjBKQxTGyJggQTiTgrLN4YM8eGhoaaGlpYcGCBbS0tFBfX3/Mc/zxj39kx44d3HrrrfT29jIwMIDP52PLli1HD5Lm0LnP4/HQ09NDMpk85s0vhBg+YX8orfWotcfR+hUNwxgWjCOvy+CcqRmtJhhJJKj75jfZcfgwN65bx9atW9m8eTNbt25lw4YNxxx/zz335K/ffffd7Ny5c3gAgjSHzgdut5toNEo8HpcQFGIKhjaNjjQyIK3rVv/jyCW+rDAc6zIfa5Ktra2sXr2aaDSKYRh8+9vfZteuXfj9/lGPD7hcnH/SSTy6fz/L169n+1e/yp133smiRYu49957Adi5cye33XYbd9xxx+QKUYDRoWq27SWm1HFa6zfLXYySikaj9Pb20tDQIB3/QpRAJpPJX6yQHHoZ+blp1SStQBwtKKU2Cd97/nk+/tBDvK+piYc/+MGZP+HDH4I9P4Z1P0Sd8pHntdarp/oUUhOcBbxeL729vfT19VFVVVXu4ggx51nBNZaRoTj0MjAwMOrkf8MwxgxL67phGHM6LNevWMH/eOghHj9wgGgyiX+mrVsFqAlKCM4Cpmnicrno6+vD5/PN6TeJELPBRCFpNbdms9ljQtLaxzGbzY66s8LQQLR+jnV9tmnw+TjnxBPZcfgwj+zbxwdOPXVmTyh9gvOH1+ulq6uLRCKB2+0ud3GEEOOw+iMnMjQkretDf44XlkqpfCiODMnR7q+UL88bV65kx+HDPLBnTwFC0Fo2TUJwznM6ndhsNuLxuISgEHOEFVQTrbqSzWaHhePI6xMFJhwbmpO5FCM4N65axacfe4xH9u0jkU7jmsl2cfnmUJknOC94PB6i0aisJyrEPGOF0mRql0ObYse7pFKpUQf5DDVecI72mHXfeOG5uLqatzU28mJrK48fOMCly5dP6zUBZJ7gfOPxeIjFYvT19REIBMpdHCFEBZpsU6xFaz1uWA59PJVK5a9PVIbRgtK6fenSpbzY2srPX3mFtYsWHROgk66BSp/g/GKtJ2qNEp2NHeNCiMqilJpwoM9oRgvJkbet69YqPtbt9zQ28i/AL5qbaTvrLGwjPstGhuhYPx1ZhR1ID/SPWsbJkBCcZWQ9USFEJbBqd9PR0NDA8poamru72d3Xx7tPPHFYaI72c2iIWk24voE0dqA/Hpn2v0NCcJaR9USFELOdaZpsXLWKrz/9NA+/9hoXTbFfUGuduxwOAuB2Tn+MhLSnzUIejye/BqIQQsxG1h6DD+zZM+7gnNFYzaGmPTfZ3qbG76Mcj4TgLOR2uzEMQzbcFULMWquPO47jq6o4Eo2y881pLoVZgHmCEoKzkFIKr9dLIpE4ZrsYIYSYDQyl2LhyJXB0x/kpK8A8QQnBWcrn8+FwOOjp6SGdTpe7OEIIMWUbB5tE759GkyhQkCkSEoKzlFKKYDCIUopwODy9PyAhhCijdy1aRI3bTXNXF7s7O6f+BAWYLC8hOIuZpkl1dTWpVIpoNFru4gghxJTYDIP1K1YA8MB0mkTNwT5BqQnOXy6XC5/PRzwel/5BIcSsc4XVL7hnz9R/OV8TlD7Bea2qqgqHw0EkEpH+QSHErLJ26VK8djsvtLRwqKdnar8sfYICcv2D1dXVAPT09Ej/oBBi1nDZbLyvqQnIzRmckgJsqishOEfYbDaqq6sZGBggFouVuzhCCDFp1lSJKYdgfp6gNIcKcv2DXq+X3t5eEolEuYsjhBCTcsny5ThMkx2HDtE+lUVApDlUjOT3+7Hb7fT09JDJZMpdHCGEmJDf6eT8JUvQwPa9eyf/i5XeHKqUulgptVcptV8ptXmUxz+tlNqllHpZKfWEUmpRMcszH1jzBwG6u7slCIUQs4K1luiUVo+p5HmCSikT+A6wDjgZuFYpdfKIw14EVmutTwPuA75RrPLMJzabjWAwSDqdprOzk1Rq+n8gQghRCutXrMBQiidef53IZLtz8vMEK7NP8Cxgv9b6gNZ6APgpsGHoAVrrJ7XWfYM3nwEWFrE884rT6SQUCgHQ2dkpfYRCiIpW7/VyzoknMpDJ8Mi+fZP7pQrvEzwe+OuQ20cG7xvLDcAvi1ieecdutxMKhbDZbHR3d8uuE0KIijblUaKV3Bw6FUqpDwOrgW+O8fjHlVI7lVI7S1uy2c80TUKhEC6Xi0gkQjQalXmEQoiKZIXgI/v20T+ZbpwKrwm+AZww5PbCwfuGUUpdAHwBWK+1HnWXWK317Vrr1Vrr1UUp6RxnDZaxpk/IgttCiEq0qLqaMxYsIJ5K8fiBAxP/glnZ8wSfA5qUUkuUUg7gA8D2oQcopd4GfJdcALYXsSzznlKKQCBAIBAgkUjQ2dkpI0eFEBVnSmuJVnJNUGudBm4CfgXsBu7VWr+qlPqyUmr94GHfBHzAz5VSf1ZKbR/j6USBeL1eampqSKfTdHR0EI/HpVYohKgY1h6D2/fuJZ3NjtCR8gMAACAASURBVH9wAeYJ2qb9m5OgtX4EeGTEff845PoFxTy/GJ3L5aKuro5IJEIkEqGvr49AIIDD4Sh30YQQ89yqUIgVtbXs7eri94cO8d4lS8Y+uJJrgqKy2Ww2amtrCQaDZLNZOjs7ZZUZIUTZKaWOjhKdaOK8bKUkZsrtdlNfX4/P56O/v1+aSIUQZWetHvPAnj1kx/ssMkxQM4sxCUGBUgq/309dXR12u51IJCIT7IUQZbP6uONY6PfzRizGzjffHP9gqzY4TRKCIs9qIq2pqSGbzdLd3U17e7vUDIUQJTW0SXTCtUQlBEWhuVwu6uvrCQaDGIZBJBKhra2NaDQqfYZCiJIYGoLjfgk3Zzagr6ijQ8XspZTC7XbjdrsZGBggHo/nL9a+hTKaVAhRLOcuWkSt282+7m52dXRwSn396AdKTVAUm8PhIBgMUl9fj9frJZlM0tnZSXt7O7FYTHapEEIUnM0wWL9iBTDBWqISgqJUTNPE7/fT0NBAdXU1pmkSi8Xo6OjIB2I6nS53MYUQc8TQUaJjMmcWgtIcKqZMKYXH48Hj8ZDJZEgkEiQSCWKxGLFYDLvdjsvlwuVyYbPZUEqVu8hCiFnogpNOwudw8EJLCwd7elhcXX3sQcbMumWkJihmxDRNvF4vtbW1NDQ0EAgEUErla4htbW2Ew2Hi8bjUEoUQU+Ky2XhfUxMA28aqDc6wJighKArGCsRQKERDQwPBYBCXy8XAwACRSIT29nba2tro6emhv79fRpoKMQvdcsstrFq1iiuvvJI1a9bgdDq5+eabp/Qc3d3drF27lqamJtauXUs4HD7mmD//+c+sWbOGpzdvhltv5btbt47+ZDPsE1Szbf6XUsdprSeYPCkqTjqdJplMkkwmGRgYIDu4MK5pmtjtdhwOB3a7HbvdjmHIdzMhKtXKlSt5/PHHcTgcHDp0iG3bthEMBvnsZz876ef4/Oc/T01NDZs3b2bLli2Ew2G+/vWvDzumubkZpRQNJ55I6ItfJPVf/8Xe3btZvnDh8Ce752xo/RPqszw/ne325NNGlITNZsvvYNHQ0EBdXR2BQACn00k6nSYajdLV1UVraysdHR309PQQj8dJJpP5wBRClNeNN97IgQMHWLduHffccw9nnnkmdvvUa2IPPvggmzZtAmDTpk1s27btmGOWL19OU1MTfqeTtaefDl4v9z733LFPJvMExWyjlMrX+izZbJZUKsXAwACpVIpEIkFfX1/+cdM0sdls2O32YT9l0I0QpXPbbbfx6KOP8uSTTxIKhcY87txzzyUWix1z/80338wFF1xAW1sbCxYsAKCxsZG2trZxz3t6Os0jmQxP9/Ye++AMm0MlBEVFMAwDp9OJ0+nM35fJZEin06RSqfzPkUu4WeFos9mOuS4BKUR57NixY9LHKqXGfa+2tLTws698BXX55Txx8CCRRIKAy3X0AAlBMVeZpolpmsOCUWtNJpMhlUrlQzKdTtPf3z+s2VQphWEY+UC0LkNvCyGKY6KaYENDAy0tLSxYsICWlhbqx1gNJhqNcskll7DlX/+V/+zt5XeHDvHIvn1c+5a3HD1I5gmK+UQpla/tjZTNZvOhaAVkJpMhmUweMxLVCsmhAWma5rD7DMOQ2qQQ0zBRTXD9+vVs3bqVzZs3s3XrVjZs2HDMMQMDA2zcuJHrrruOq666ijeeeYbfHTrE/Xv2DA/BGc4TlBAUc4ZhGDgcjlHXNLVqkEPDMZvN5muViURi1EV6rVA0DGPY9aH3WRcJTDGftLa2snr1aqLRKIZh8O1vf5tdu3bh9/sn/N3Nmzdz9dVXc+edd7Jo0SLuvfdeAHbu3Mltt93GHXfcwb333svvf/97urq6uPvuu0llMvDOd/JLu53+VAq3NaZApkgIURhWKA4NyNF+jvWeGRmKY12sWqgEpxBTs/r223m+pYUHP/CB/LqiPPJh2H3PtKdISE1QiEFWME005DubzQ67WOE48r5UKjVuaMLRZtmhwTj09mR+CjFfXLFqFc+3tPDAnj1HQ1AGxghRWlZQTZbW+piQtMJx5G0rPK3HJlsea4TdWNdHuz3WfRKsolJtXLmSL/zmN2zfu5d0NovNMGSeoBCVTik17RGpQ8NytJ9j3ZfJZPLXrctUyluIy0TPZT0uxGStqqtjZSjEns5Ofn/oEO9dskRqgkLMZVaNc6ZTOoaG4dCwHHkZ77HxHp+uscJy5H3j3R7rsbGOmei6qGwbV67ka089xf27d0sICiEmZ+gHfTHmSI4XnFZITuWxofdZzcJjPV5oEwXpTI4Z7Wehfmei4+eKK1at4mtPPcW2PXu4Zd06DAlBIUS5lbMvcaxgHO96Ie4b+nPoAKiJfpbDVMOzUq6P9tgpwSALq6o4Eovx9MGDrMGYUZBJCAohZrXZNphnsmE5XogW677JPD50wNZEzzHe9Zm46MQTufPVV/nZSy/xVneaiWcmjk1CUAghSmguN1VO1nQCc+jta976Vu589VV+dfgw3zpjJhEoISiEEKLExmv+nIz3LF1KyONhfzhM84CbU2ZQFtlPUAghxKxiMwzWL18OwP3tM2tilRAUQggx61yxahUAD7SmZ/Q8EoJCCCFmnfNPOgmfw8GLkTSvZ6un/TwSgkIIIWYdl83GJU1NAGxLr5z280gICiGEmJU2rsyF3/3pVdN+DglBIYQQs9L7mppwGIqnsydO+zkkBIUQQsxKVU4naxeG0Ex/zqWEoBBCiFnritPXzOj3JQSFEELMWutXrOC6t7512r+vyrmo63QodZzW+s1yF0MIIUQFUUo9r7VePdXfk5qgEEKIeUtCUAghxLwlISiEEGLekhAUQggxb0kICiGEmLckBIUQQsxbEoJCCCHmLQlBIYQQ85aEoBBCiHlLQlAIIcS8JSEohBBi3pIQFEIIMW9JCAohhJi3JASFEELMWxKCQggh5i0JQSGEEPOWhKAQQoh5S0JQCCHEvCUhKIQQYt6SEBRCCDFvSQgKIYSYtyQEhRBCzFtFDUGl1MVKqb1Kqf1Kqc2jPO5USv1s8PFnlVKLi1keIYQQYqiihaBSygS+A6wDTgauVUqdPOKwG4Cw1noZ8C3g68UqjxBCCDFSMWuCZwH7tdYHtNYDwE+BDSOO2QBsHbx+H3C+UkoVsUxCCCFEnq2Iz3088Ncht48AZ491jNY6rZSKALVA53hPrNQ/F7CYQggh5qtihmDBKKU+Dnx88GYS/umVcpZnlgoxwZcLMSp53aZHXrfpk9duelZM55eKGYJvACcMub1w8L7RjjmilLIBAaBr5BNprW8HbgdQSu3UWq8uSonnMHndpkdet+mR12365LWbHqXUzun8XjH7BJ8DmpRSS5RSDuADwPYRx2wHNg1evwr4jdZaF7FMQgghRF7RaoKDfXw3Ab8CTOD7WutXlVJfBnZqrbcDdwI/VErtB7rJBaUQQghREkXtE9RaPwI8MuK+fxxyPQG8f4pPe3sBijYfyes2PfK6TY+8btMnr930TOt1U9L6KIQQYr6SZdOEEELMWxUbgrLk2vRM4nX7tFJql1LqZaXUE0qpReUoZ6WZ6HUbctyVSimtlJLRe0zudVNKXT34N/eqUurHpS5jJZrE+/REpdSTSqkXB9+r7ytHOSuNUur7Sql2pdSo0+RUzi2Dr+vLSqkzJnzOSmwOHVxyrRlYS26S/XPAtVrrXc8//7zDMIxPRCKRzyqlAtXV1d39/f3eRCLhCQaDHWUteAVob28/vqamps00zUxnZ+eCYPD/sXfmYU0d3eM/N/sOSYAEgllMWEQIq1qUahVcqrgg1aLWFvwqiqgV3FBUWkXRWqxYWy3yVou/11YtKri0fUVqLdjXtSpS2SHsW1hChOz5/dGGNyAgLlWr9/M8PJp7Z+bOzL13zpwz585hNuJwOK3pvFqtJhEIBDWCIMYHDx7QNRoNCe23R/cbAIDRaMTI5XIbAEAYDEYzgUBQv6DqvjQ8qt90Oh2+paXFis1m12MwGIPBYMBiMBj9i6zzy8Cj+q21tZWNx+M1VCq1XafT4eVyuQ2Hw+n5iVl/GBEEadPr9YcMBsN+b29vzd/QjOcOgiCjAUAJAKlGo9G1l/OTAWA5AEyGPzdnSTIajT03aenGy/qxfNeWawAACIKYtlz7A4fDHWQwGKOIRKIFj8ertrCweGAwGJru3LnjLpVKm17nXdcUCgWVSCR2ODs71wEAVFVVYQEA7O3te/3wVqlUPqioqBC4uLi81h/mDrTfysvLB/H5/Mr6+nquvb19C51O73gR9X1ZGEi/yWQyex6PV8vlcl/rZ8ycgfRbaWkplUAgqO3t7ZsUCgWVTCZbPM57ajQaQaPR4GtqapYrFAov+N+naP9ojEbj5UdY/abDnwLSCAD/RRDEEkEQW6PRWNtXhpfVHNrblmu8v/7vJxAI2vR6PZ5EImkAADAYDGCxWL1Op3tZhfpzQaPREPB4fNeMj0AgaLRaLaGv9I2NjdZ0Or3t+dTu5WUg/dbe3k7RaDQEFov12veXiYH0m0qlIqrVatIff/zhnJeX59zc3Mx4/jV9uRhIv/F4vJqWlhbW7du3pcXFxQ6DBg2qeJxrIAgCRCJRKxAI2gDA7xlV/Z9Af7KjV15WIdgfWAwG8/LZcP9hNDQ0sDo6Oig8Hq/uRdflZcdoNEJVVdUgPp9f+ejUKD1A1Go10dnZuWDw4MGlFRUVQp1Oh33RlXrZkcvlLBaLJffw8LgrkUiKysvLRU+ydPXXWIn2dz+8rELwkVuu4fF4jVqtJgAAGAwG0Ov1WBwOp3uOdXzp6Dmj7DnjNNHS0kKvr6+3dXBwKEYnFI/uN71ej1WpVKSCggKnO3fuuHV0dFBLSkok7e3tlBdT45eDgTxveDxeY2Fh0YrBYIxkMllDJBJVnZ2dxOdf25eHgfSbXC63YrPZzQAADAbjgcFgwGi12tfa0jVABrJdZzdeViH4yC3XLCwsWpuamtgAAHK5nEmj0dpf5vXA4OBg4dixYyV/5zWam5vVoaGhdDKZ7IUgiHdrayuLyWS2mqdRKpXkyspKgVgsLiYQCK/1pMEEjUZ7oFarSZ2dnQSDwYD07DccDqf39PS84+7unuvu7p5LoVAeiMXi4td9TfBR/QYAYGlp2dre3k4HANBqtTi1Wk0ikUivtUPRQPoNj8dr2traGAAADx48IBmNRgSPx6Pv66PJAID3//ISfQMA2vpbDwR4SR1j+tty7b///S8FAMDGxqappKREdPfuXVcsFqsfPHhwyQut9CNITk6u/Ls9cbdv385tamrSHjt2DKhUqtHS0rJ5//79tGPHjjnk5+fjlEol5qefflJaW1tjS0tLxQB/vmxOTk7Ff2vFXnIwGAwMGjSooqioyBEAgMViNVGpVFVFRYUdlUp9wGaz0XXAXhhIvzGZTIVCoWDk5uYOBQAjj8erxOPxr7V36ED6bdCgQZUymUzY2NjIAQAQCATlL/Mk/3mBIMi3APAWAFghCFIFAHEAgAcAMBqNB+DPHcomA0AxAHQAQNgjy3wZP5Hojzt37pS7u7ujnma9EBAQIGYwGPqTJ0+Wm45t2bLFprOzE0Mmkw1xcXGD8vPzc52cnJ67u7RKpUJIJNI/62FDQXkFuHPnjpW7u7vwRdfjZeVlNYe+cvQ0h/7www80d3d3ZwqF4kmn0z3c3NyGXL9+nWQ6/80331g6Ojq6EAgELy6XK123bh3XYDD0WT6Px3O7ePGi5alTp9gIgngHBwcLAQA2b97ckJCQUPfWW28pH6e+n3/+Odt0fTab7T5z5kyh6RyCIN6HDh1i9rz+5s2bOeZpEhISrCdMmCAmk8mey5Yt43G5XOm2bdtszPPdvXuXiCCId3Z2NgUAQC6XY+fMmSNgsVjuVCrVc9iwYU6XL1/uWnuTy+XYGTNmiFgsljuRSPSyt7d327JlS7cyUVBQUAbKS2kOfdXRarXw7rvvSkJCQpqOHj1aptFokKtXr1Kw2D+duH799VfKggULxCtWrKgNCwuT5+TkUFetWiVgMBiG2NjYht7KvH79+v133nlHxGQy9fv376+gUqlPrHXt2rXLauPGjfz169dXBwUFtSoUCux//vMf+hOUY7dx48bqPXv2VGIwGMDhcHD8+HGWeRsOHTrEHjx4sMrPz6/DYDDAhAkTJHQ6XZ+WllZkbW2tP3jwIHvy5MlOeXl59wQCgTY6OtouPz+fnJaWVsTj8XSFhYXEhoYG9DlGQUF5Il6JwaO5uRmv1Wqfq1aLx+MNLBZL++iUD9Pc3Ixtb2/HTp8+vXXo0KFqAABPT0+V6fyuXbs4w4YNa//ss89qAACkUqm6qKiItHfvXm5fQtDOzk5HJBKNJBLJwOfzn2oB/dNPP7X7v//7v/qPPvqo3nTszTfffGwnkKlTpzZHR0d3ma7DwsLkX331FScvL49oavfJkydZc+bMaQIAOHv2LP3+/fuUhoaG2zQazQgAkJSUVPPTTz9ZHjx4kBUfH19fWVlJdHNz6xg7dmwHAICjo+MrsRMGCgrKiwE1h74AOByOPjg4WD5z5kzHt956S/LRRx9xioqKulymi4uLSSNGjOhmvhw9enR7Q0MDvrm5GRMTE8OlUCiepj/zvE9LdXU1rqGhAT9+/Pj2py3Lx8enm+AcMWJEp4ODQ+ehQ4dYAABZWVnUyspK4oIFC5oBAK5fv05RqVQYGxsbD/P2FRcXk0tLS0kAAEuWLGk4e/Ys08nJySU8PNz+3LlztKetJwoKyuvLK6EJPqlG9iL5/vvvy3/77bf6s2fPWpw/f95yx44dvH//+9/FwcHBiv7yIQgCUVFRje+9916L6ZhQKHyu2hCCINDToUqr1T7kukaj0R5axJw9e7b8//2//2f96aef1qamprK8vLyUJm3OYDAgbDZb+/PPPxf0zMdkMvV/5Ve8+eabuSdPnmRkZWUxZs2a5TB58uSW77//vvxZtQ8FBeX1AdUEXyC+vr6d27Ztq7t27VrB8OHD2w8fPswGAJBIJKqrV69203AuX75M53A4WiaTaeBwOHpXV1e16Q+Pxz+zOvF4PJ2NjY32woULfa4BMplMXU1NTddFKysrcU1NTQOqRFhYWHNlZSXx4sWL1DNnzrBCQkLkpnM+Pj4dcrkcj8FgjObtc3V1VfN4vC4Tr62trS4yMrI5LS2tPCkpqfzkyZPszs7Of6z/+I0bN7zv3bvnkpubO7SgoEDyrHdUqa+vZ5eVlfEBACorK+2qq6s5j8qDgvK68Epogv808vPzCXv37rUOCgpqFQgE2oKCAmJ+fj45NDS0EQBg7dq19WPGjBkSHR1tFxoaKs/JyaF+9dVXnA0bNjzOLvIAAFBRUYGrqqrC//HHHyQAgNu3b5PlcjlWLBZrOBxOr99rRUdH127evHkQh8PRBgUFtf31fSHj448/rgcAGDlypCIlJcVm9OjRSiwWa1y/fr09kUjs23XVDLFYrB02bFh7ZGSkQKlUYj/44IMujXb69OkKT09P5YwZMyTx8fFVUqlUVVVVhT937pzFxIkTFZMmTVKuXLnSztvbu8PDw6NTq9Uip0+fZtrb26vJZPI/9vMLDAZjcHV1/QMAoLi4WFhXV2dtb2+PbmeHgvIcQIXgC4BKpRqKi4tJ8+bNE7e2tuLYbLZ25syZzVu3bq0DAPDz8+v4+uuvS7Zt22b3+eefc9lstm7ZsmW169ev79Uppj/27Nlj89lnn9mafoeEhEgAAJKSkspXrFgh7y3PunXrGgkEgnHfvn2c+Ph4ewsLC/3YsWO7Phj//PPPq95//33hpEmTnNhstnbr1q1VJSUlpN7K6o2QkBB5VFSUcPz48a3W1tZdghiDwUBmZmZRdHQ0b/ny5cLm5mYcm83W+fj4KBcuXCgHACASiYaPP/6YV11dTSAQCEYPDw/l6dOnX5mP/Wk02oOOjg6y6Xd1dTWntbWVZTAYEAsLi1Y+n18DANDQ0MCur6/nIAgCJBKpUyKRlMnlcou6ujpbo9GIwWKxOrFYXIruCoSC0j/ox/IoKC+YW7dueXp5ef1uNBqhqKhosJWVVROLxVK0tLQwWlpamCKRSAYAUFhYKOFyuXU4HE5XUlIiGTJkSD4ej9dptVosHo/Xa7VaLA6H0yMIAnV1dVYqlYokFAqr6uvr2R0dHVSRSFRRWVlph8Fg9Dwer/5R9UJ5NUA/lu8fVBNEQXnBGAwGzL1791y0Wi2eSCSqmEymAgCgra2N0d7ezsjLy3MxpVOpVCSDwYCxtLRsMe0ladqGTKPREEpKSuy1Wi3eaDRi0KC/KCiPBhWCKCgvGNOaoF6vxxQUFDjU1tba2NnZNQAAcDichwLS1tbW9rpDTkVFBd/GxqaOzWa3tba20mtra+2eR/1RUP7JoN6hKCgvCVgs1sDn8ysaGxs5BoMBLCwsFHK53Eqn02EAANRqNV6j0eAYDIaitbWVqdVqsQAApn/1ej2WQCBoAQDkcjn7xbUEBeWfA6oJoqC8RNBotE4SidTZ1NTEsrGxae7s7CTdv3/fGeBPjXHw4MFlVCpVxeFwavPz850RBDGSSKQOiURSbmtrW1NaWirGYrE6Go3WDgCvddw+FJSBgDrGoKCgoLzCoI4x/YOaQ1FQUFBQXltQIYiCgoKC8tqCCkEUFBQUlNcWVAiioKCgoLy2oEIQBQUFBeW1BRWCrzgFBQUEBEG8L1++TBlI+uHDhzu9//77/L+7Xi8SHo/ntnnzZk5fv3vj008/tbK1tXXDYDDe0dHRz/UjdARBvA8dOsR8ntd8Fpw7d44mFApddTp0+9JnzXfffWfh7Ozsotf3ugc+ymOACsHnRHBwsBBBEG8EQbxxOJyXvb29W3h4uL1Cofhb74FYLNbIZLI7vr6+A4oMf+bMmeKkpKSqv7NO/zQaGxuxMTEx/GXLltWXlZXdjYuLe60iPBQVFRHGjRsnIZPJnkwm0z00NHSQSqV6ZOiqmJiYQatWrarF4V7dz5EbGxuxM2bMENHpdA86ne4xY8YMUVNTU7+hsAwGA0RHR9vZ2NhISSSS1/Dhw51u3LjRbQP6R5UbEhLShsVijQcOHGD9XW17XUCF4HPE19dXIZPJ7uTn5+du3Lix+siRI9YRERH2faVXq9VPHSMPh8MBn8/XDTTmIIfD0TOZzAGFRXqWDGRQfVEUFRUR9Ho9Ehwc3CoQCLQWFhZP1D8vcxv7QqfTwZQpUyRKpRJ74cKFgq+//rrs/PnzzMWLF/f53AIAXLhwgVpWVkYKCwtrfprrv+x9FhwcPPjevXuUU6dOFZ06daro3r17lNmzZ4v6y7Np0ybuV199xfnkk08qfv311z+srKy0kydPdmxpacE8Trnz5s1r2r9/Pxob8ilBheBzhEgkGvl8vk4ikWiXLFnSPH369Ob//Oc/lgAAZ8+epSMI4n3s2DELNze3IXg83uvkyZMMAICjR49aDB06dAiRSPTi8Xhuy5cv55kPDiqVClm2bBnPzs7OjUAgeNnb27vFx8fbADxsDlWr1UhoaOggGxsbKYFA8OJyudKlS5fyTGX1NIc2NjZiZ86cKWQwGB4kEslr5MiRjuaz1r1797IpFIpneno63cHBYSiZTPYcMWKEY35+PqG/vggODhaOHTtWEhsby+VwOFI7OzupqS0RERE8DocjJZPJnq6urkPS0tIY5nl///130rhx4yR0Ot2DQqF4enh4OF+7do0MAPDLL79QRo0a5cBkMt1pNJqnt7e3U2ZmJvVJ79nevXvZo0aNcgEAGDJkiBuCIN4FBQUEAIBdu3ZZ8fl8Vzwe78Xn810TExOtzPMiCOKdkJBgPWHCBDGZTPZcsWIFr7drGAwGiIuL4wgEAlcCgeDF4XCkkZGRvaYFAFi6dClPKBS6kkgkLx6P57ZkyRL7jo6OruehuLgY7+/vL7awsPAgk8meIpFoaHJycpc5dfXq1bamZ8XKyso9KChI2Ne1Tp48ySguLiYfPXq01M/PryMoKEjx0UcfVX333XfWzc3NfY4fR44cYfv6+ipoNFrXbhx5eXlEf39/sZWVlTuZTPZ0cXEZ8u2331qY5+PxeG7R0dF2s2bNEtLpdI+goCARAEBZWRk+MDBwMIPB8GAwGB5vvfWWJDc3l/g4ZT9rbt26Rfr1118ZX375ZXlAQMCDgICAB1988YXs559/trhz506vu/UYDAZITk62WbZsWW1oaGjrsGHDVMePHy978OABNiUlhfU45c6aNastLy+Pcu/ePXRnoKfglbBTIMjH3i/iukZj3M2nyU8ikQw6na7bTHfTpk28HTt2VDk7O6ssLS0NaWlpjPDw8MHbtm2rCAgIUJaWlhKWL18uUKvVSHJychUAwKxZs4TXrl2j79y5s2L48OEdJSUlRJlM1qsQ2r59u82PP/7I/Oabb0olEommvLy8K+Bub4SEhAhLS0tJx44dK2az2fqYmBheYGCgY3Fxca5pgNNoNMjOnTttk5OTyygUijE0NFS0cOFCQXZ2dlF/7b927RqdTqfrz549W2g0GhEAgNmzZwtlMhnx8OHDpQKBQHP69GmLkJAQyeXLl+/7+vp2lpeX48eNG+fk5eWlzMjIKGSz2frs7Gyqad2pra0NO3fuXLmvr28lgiDw2Wef2cycOdOhsLAwl8vlPvYCyoIFC5p5PJ72nXfecbh06dJ9kUiksbOz06WmplquX7+ev2XLlsrAwEDFmTNnLNatW8e3tbXVzp07tyv24q5du+w2btxYvWfPnkoMpneZsXz5cl5qaqr11q1bKwMCApR1dXW4Gzdu9LmGS6VSDcnJyWUCgUB7+/Zt8sqVK/lEItGQlJRUAwAQHh4uUKlUyI8//ljAZDL19+7d67q/hw8ftjxw4AA3JSWl1Nvbu7O2thaXnZ3d5yThypUrtMGDB6skEonWdGzGjBmKyMhIJCcnhzp16tT23vJdvXqVNmPGjG5aoEKhwEycozDXGAAAIABJREFUOFGRkJBQQ6FQDEeOHGG+//77Ymdn5z88PT1VpnTJycmclStX1sTFxdUajUZob2/HjB071snb21t54cKFAgKBYNyxYwdn4sSJjgUFBXl0Ot0w0LLNKSoqIri7uw/tq+1/tVV+9OjRit7OXb58mUqhUAwBAQEPTMfGjx+vJJPJhkuXLtHc3d0fiuKRn59PaGpqwk+ePFlhOkaj0YzDhg1r/+2332gA0DTQch0cHDRsNlt34cIFuqurKxox5Al5JYTgP5Gff/6Zkp6ezho5cmS3QWTDhg01M2fO7HpBEhISbCMiIuo+/PBDOQDA0KFD1a2trVWLFy8WHThwoCovL4949uxZ1okTJ4reeecdBQCAi4uLpq/rymQygkgkUk2cOFGJwWDAwcFBM378+Ae9pc3NzSVmZWVZnj9/vuDtt99WAgCcOHGiTCgUuiUnJ7Ojo6ObAAD0ej2yf/9+menlXLFiRd2HH34oNBgM0NfADwBAIBAMx44dKzdFhTe1paCgINfBwUHzV1sas7KyGF988YW1r69vRWJiog2ZTDacO3eulEQiGQEApFJp1wAwbdq0bv15+PDhCg6HY3ny5EmLpUuXPrZpjkajGa2trXUAAFwuV8fn83UAAHv27OEGBQU1b9iwofGvOjTcunWLkpiYyDUXglOnTm029VNvtLW1YVJSUjhbt26tXLlypRwAwNXVVW0+APZk165dtab/Ozk5aWQyWd2+ffs4JiFYVVVFmDp1aouvr28nAICzs3PX81BeXk60srLSBgUFKYhEotHBwUEzevToPteL6+vr8VZWVlrzY1wuV4fFYqGmpqZPG3tNTQ3Bzs6uWz5fX99OU50AAHbu3Fn3448/Wn777bdMT0/PrjYNHz68PT4+vive4Z49e9hGoxFOnDhRbnqe/v3vf8usrKw8jh07ZrFw4cKWgZZtjlAo1Fy7du2PvtoAAMBkMvucONXV1eGZTKbO/BnHYDDAYrF0dXV1vfZNdXU1HgDAzs6um7eQtbW1rra2Fv+45drY2GjKy8v7tbqg9M8rIQSfViN7Xvz6668WFArFU6/XIzqdDvH3929NTk7uNsscOXJktwEpLy+PkpubS/3iiy+4pmNGoxFUKhWmsrISf/XqVQoGg4EpU6b0OiPvycKFC+WBgYGOIpHIdfTo0YopU6a0zZo1qw2LfXgt/+7duyQMBgPjxo3rGpDZbLbe0dGx01x7JBAIRvNZr729vVar1SKNjY1YhUKBNZ9tr1ixonbHjh11AACOjo6dJgEIAHD16lWK0WiEnrNzjUaDvPHGG+1/1Yns4+OjNAnAnlRXV+PWrFnDu3LlCl0ul+P0ej2iVqsxFRUVz3SgKCkpIc2fP7+bcBs1apQyMzPT0vyYj49Pvw5Jt27dImk0GuTtt99W9JfOnEOHDjH37dtnI5PJSB0dHRiDwYCYewlGREQ0rFmzhn/x4kWL0aNHK2bNmtX65ptvdgAAvPfee83Jyck2fD7fbcyYMW2TJk1SzJkzp9X8PjwL1Go1puc9UigUmLVr19pduHDBorGxEa/T6RCNRoNxcXHpNE/n6enZbQJw8+ZNanV1NZFGo3maH1epVJiSkhLi45RtDh6Ph3+6BkUikYydnZ3ostZT8EoIwX8KPj4+7SkpKTICgWAUCARaIpH40MBDp9O7OV0YjUYkOjq6Zt68eS090/acaQ8EPz+/jrKysrunT5+2yMzMpEdERAiTkpI6s7OzC3sThH2BIP+z4mKxWGNv5wwGA9Jztm3SqgAAKBRKt7bq9XpAEASys7PvEwiEbmVSqdQBOaPMmTNH1NTUhNuxY0elRCJRk0gk44QJExw1Gs1zGSjM+wUAgEajPVMno4sXL1IXLVo0OCoqqiYwMLCSxWLpv//+e8stW7Z0OapERUU1TZs2re3UqVMWWVlZDH9/f+dly5bV7d69u0YikWiLi4vvZWRkMC5cuMCIjY21T0hIsLt58+Z9BoPxUF05HI72+vXrNPNjdXV1OL1e3+/zZ2lpqWtpaen2QEVERNhfunTJYtu2bZVDhgxRU6lUw/z580UajaZbp/W81waDAZydnTu+++670p7XMT1PAy3bnKc1h3K5XG1LSwvO3OJhMBigubkZx+Vye+0bHo+nBQCoqanBmawdAACNjY04Gxsb7eOW29raijV/p1AeH1QIPkfIZLLhcWeeLi4uHQUFBaS+8g0fPrzDYDDAuXPn6CZz6KNgMpmGsLCwlrCwsJasrCy5v7+/c15eHtHcrAgAIJVKVQaDAbKysqgmc2hzczOmsLCQ/N5778kHcq3HmW2PGDGiw2g0QnV1Nb6vtSapVNqZlpbGUqlUSG/a4M2bN2nbt2+vCAkJaQMAqKysxDU2Ng7MNfYxEIvFqitXrtCioqK6tMGcnByaWCzuU/PoDQ8PDxWBQDD+8MMPDDc3t8ZHpb98+TLNxsZGY24STUxMfEjLFYvF2tWrVzetXr26KTY2lnvw4EGb3bt31wAAUCgUY0hISFtISEhbZWVlLZ/Pd8/MzKSZm+FNjBw5Url3717bkpISvFgs1gIAZGRkMAgEgnHUqFF9mmyHDh3a8ccff5DNj12/fp02e/ZseWhoaCsAQEdHB1JRUUEcPHhwr2t2Jry8vDoyMjJYXC5XZ2Vl1at58knKflpz6OjRox90dHRgLl68SDUtKVy8eJHa2dmJeeutt5S95XF2dtZYWVlpf/jhB8aYMWM6THW9ceMGPS4urvJxyu3o6EAqKyuJj7I2oPQPKgRfcmJjY2veffddycqVKzXz5s1rxuFwcPv2bfLVq1epBw4cqJJKperJkye3REZGCpVKZcUbb7zRUV5eTigpKSFERkY+tAb20UcfcWxtbbXDhg3rIBAIxiNHjrBoNJpeJBI9NHN1c3NT+/v7t0ZGRgq+/PJLGYvF0sfExPCoVKph0aJFAxKCj4NUKlVPmzatefHixcKmpqaqESNGPGhqasJlZmbSxWKx+oMPPmiNjo5uOHLkiHVgYODgTZs21bLZbH1OTg7Vzc2tc+TIkZ1CoVD13Xffsf38/B4olUrM2rVr7fF4/DOPFxYVFVUXFhY2OCEh4UFgYKAiIyPDIj09nZWamlryOOUwmUzDggUL6uPj43lEItEQEBCgbGhowF67do26bt26h4Sik5OTqqGhgbB//37WmDFjlGfOnLFIT0/v9q1YWFjYoClTprS5uLioWltbsZmZmQyJRKIC+NPbVafTIX5+fg8YDIY+NTWVhcPhjC4uLr0Ki5kzZyokEknnvHnzRImJiZWNjY24zZs324eEhDSyWKw+tdyAgADFkSNHunnLikQi9blz5yyDg4NbCQSCcfPmzbb9aWomwsPDmz///HPu22+/Lfnoo4+qxWKxpqysjHDy5EnLFStWNLq5uamfpOynNYd6eXmp3nzzTYXp/QAAiIyMFIwdO7bNtDxQVlaGHzdunOPHH39c/f7777diMBgIDw9v2Lt3L3fIkCEqFxcX1ccff2xHoVD0CxcubB5ouQAAWVlZNAKBYAwICOhV4KIMDFQIvuQEBwcrjh8/Xrxt2zbbr776ioPFYkEoFKrmzp3bpYF8//33ZVFRUby1a9fyW1tbcRwORxMREVHfW3l0Ol2flJTEkclkJARBYMiQIR0nT54s6mmGNfHtt9+WL168eNDs2bMlGo0G4+XlpTx79myhuev7s+T48ePl69evt920aZN9fX093sLCQi+VSh+MHz++HQBAJBJpMzMz81etWmX/9ttvOyEIAo6Ojp3JycnlAAApKSnlixcvFowaNcrF2tpaExMTU7Nnz55nrgnOnz+/tba2tnLfvn2cTZs2DbKzs9Ps3LmzwtwpZqDs27evmslk6nft2mW3evVqPJvN1s2aNavXScbcuXPbcnJy6jZs2DBIrVZj/Pz8FDExMTUxMTFdn7UYDAZYtWoVv66ujkChUPSjRo1q//zzzysB/tRsEhMTuXFxcfY6nQ4Ri8Wq1NTUEnPnGXNwOBycO3euODw8nB8QEOBMJBKNM2bMkO/fv7/fDRXCw8Pl8fHxvBs3bpB8fHxUAACff/55ZWhoqHD8+PFODAZDv3jx4nq1Wv1IMzWdTjf8+uuv+VFRUfbz588XK5VKrLW1tXbkyJHtJs3wSct+WtLS0koXLlzInzFjhiMAgL+/f+u//vWvLvOpRqNBysvLSa2trV2m4a1bt9Z1dnZiVq9ezVcoFDipVPrg3Llzhebf5z6qXACAo0ePsqZPny7v691FGRhoUF0UFJS/haVLl/Kamppwx48fl73ourxqVFdX44YOHer63//+94++JjAm0KC6/YN6FaGgoPwtxMfH14pEIjW6d+izp7CwkLBr1y7ZowQgyqNBNUEUFBSUVxhUE+wfVBNEQUFBQXltQYUgCgoKCsprCyoEUVBQUFBeW1AhiIKCgoLy2oIKQRQUFBSU1xZUCKKgoKCgvLagQhAFBQUF5bUFFYKvOD0jyz+KnpHlX0V4PJ7b5s2bOX397o1PP/3UytbW1g2DwXhHR0fb/f21/B8IgngfOnSI+eiULxe5ublENpvtLpfLBx6eBGXAuLm5DTl8+LDlo1Oi9AcqBJ8TwcHBQgRBvBEE8cbhcF729vZu4eHh9gqF4m+9B2KxWCOTye74+voOaKf5M2fOFCclJfW7L+TrRmNjIzYmJoa/bNmy+rKysrtxcXF1L7pOz5OwsLBBrq6uQ4hEohePx3MbaL61a9fyQkNDG9hsdp+RGP7pdHZ2Ih988MEgJpPpTiaTPceNGycpKSnpd69anU4HH374oR2Px3Mz9emKFSvstNr/7WFvGit6/s2fP79rgrp+/fqauLg4e/NYkiiPDyoEnyO+vr4KmUx2Jz8/P3fjxo3VR44csY6IiLDvK71arX7kDvuPAofDAZ/P1+HxA9tDmsPh6M038n1eqFSqp27r30VRURFBr9cjwcHBrQKBQGthYfFE/fMyt7E/DAYDzJkzRz5z5swBRw4pLi7GX7hwwXLJkiVPFW3kZe+zhQsXDvrhhx+YX3/9ddmFCxcKlEoldsqUKQ79bRW3ceNG7uHDh2127dpVcffu3XuffPJJxeHDh202bNhga0ojk8numP8dPXq0GAAgJCSkKzLM7Nmz2x48eIA9ceKExd/ayFccVAg+R4hEopHP5+skEol2yZIlzdOnT2/+z3/+YwkAcPbsWTqCIN7Hjh2zcHNzG4LH471OnjzJAAA4evSoxdChQ7tm4suXL+eZDw4qlQpZtmwZz87Ozo1AIHjZ29u7xcfH2wA8bA5Vq9VIaGjoIBsbGymBQPDicrnSpUuX8kxl9TSHNjY2YmfOnClkMBgeJBLJa+TIkY43btzoiiq/d+9eNoVC8UxPT6c7ODgMJZPJniNGjHDMz8/vN5J7cHCwcOzYsZLY2Fguh8OR2tnZSU1tiYiI4HE4HCmZTPZ0dXUdkpaWxjDP+/vvv5PGjRsnodPpHhQKxdPDw8P52rVrZACAX375hTJq1CgHJpPpTqPRPL29vZ0yMzOpT3rP9u7dyx41apQLAMCQIUPcEATxLigoIAAA7Nq1y4rP57vi8XgvPp/vmpiY2C10EIIg3gkJCdYTJkwQk8lkzxUrVvB6u4bBYIC4uDiOQCBwJRAIXhwORxoZGdlrWoA/N6YWCoWuJBLJi8fjuS1ZssS+o6Oj63koLi7G+/v7iy0sLDzIZLKnSCQampyc3GVOXb16ta3pWbGysnIPCgoS9tcH33zzTWVsbGyDg4NDv3H/zElNTWU5ODiozAPH1tXVYadOnSricDhSEonkJZFIhiYlJbHN8w0fPtxp3rx5/PDwcHsmk+nu4+PjDAAgl8uxc+bMEbBYLHcqleo5bNgwJ3MT/0DKftbI5XLs8ePHrT7++OOqoKAghZ+fX8fRo0dLCwsLyenp6Yy+8l29epXm7+/fOnfu3DYnJyfNvHnz2gICAlpv3LjR9Zzy+Xyd+V96erqlQCBQT5kypStsEg6Hg7Fjx7Z9++23rN6vhDIQXolQSmNld7xfxHV/FrjffJr8JBLJoNPpus10N23axNuxY0eVs7OzytLS0pCWlsYIDw8fvG3btoqAgABlaWkpYfny5QK1Wo0kJydXAQDMmjVLeO3aNfrOnTsrhg8f3lFSUkKUyWS9CqHt27fb/Pjjj8xvvvmmVCKRaMrLy/F//PEHqbe0AAAhISHC0tJS0rFjx4rZbLY+JiaGFxgY6FhcXJxrCqek0WiQnTt32iYnJ5dRKBRjaGioaOHChYLs7Oyi/tp/7do1Op1O1589e7bQaDQiAACzZ88WymQy4uHDh0sFAoHm9OnTFiEhIZLLly/f9/X17SwvL8ePGzfOycvLS5mRkVHIZrP12dnZVNPMu62tDTt37ly5r69vJYIg8Nlnn9nMnDnTobCwMJfL5T623WjBggXNPB5P+8477zhcunTpvkgk0tjZ2elSU1Mt169fz9+yZUtlYGCg4syZMxbr1q3j29raas3DKe3atctu48aN1Xv27Kk0RQnvyfLly3mpqanWW7durQwICFDW1dXhbty40ecaLpVKNSQnJ5cJBALt7du3yStXruQTiURDUlJSDQBAeHi4QKVSIT/++GMBk8nU37t3r+v+Hj582PLAgQPclJSUUm9v787a2lpcdnb2E08S+iInJ4fm7u7eLehuZ2cnxsPDo2P9+vV1lpaW+vPnzzNWr14tEAqFmunTp3cFUT59+jR77ty5jVlZWQVGoxEMBgNMmDBBQqfT9WlpaUXW1tb6gwcPsidPnuyUl5d3TyAQaAdadk8oFIpnf+3w8fFRXr58udfnOCcnh6LT6ZDp06d3BSOWSCTawYMHq7Kzs2nBwcG9Brn29fVVHjp0yPr3338neXp6qm7evEm6cuUKY+XKlbW9pW9ra8OcOXOGtWrVqpqe54YNG/Zg9+7dtr3lQxkYr4QQ/Cfy888/U9LT01kjR47s9oJu2LChxjzCd0JCgm1ERETdhx9+KAcAGDp0qLq1tbVq8eLFogMHDlTl5eURz549yzpx4kSRKbK8i4tLnzvLy2QygkgkUk2cOFGJwWDAwcFBY4pe3ZPc3FxiVlaW5fnz5wtMkeVPnDhRJhQK3ZKTk9nR0dFNAAB6vR7Zv3+/zBTwc8WKFXUffvih0GAwQF8DPwAAgUAwHDt2rJxMJhsBAExtKSgoyDVpEC4uLo1ZWVmML774wtrX17ciMTHRhkwmG86dO1dqiiwvlUq7Ao1OmzatW38ePny4gsPhWJ48edJi6dKlDwUZfhQ0Gs1obW2tAwDgcrk6Pp+vAwDYs2cPNygoqHnDhg2Nf9Wh4datW5TExESuuRCcOnVqs6mfeqOtrQ2TkpLC2bp1a+XKlSvlAACurq7qgICAPqO2m0eVd3Jy0shksrp9+/ZxTEKwqqqKMHXq1BZfX99OgD+jmZvSl5eXE62srLRBQUEKIpFodHBw0IwePfqZRyavrq4mSqXSTvNjIpFIu3Xr1q44ly4uLk2XLl1i/BUXr+u+8Xg89cGDB7vWpTMyMuj379+nNDQ03DZNvJKSkmp++ukny4MHD7Li4+PrB1p2Tx4VWZ5KpfZp+q6pqcFjsVjgcrndbJ9WVlba+vr6Ptcf4uPj69rb27He3t5DMRiMUa/XI8uXL6+NiYl5KIgyAMDBgwdZWq0W6c20zOPxNA0NDXitVgsDXfJA6c4rIQSfViN7Xvz6668WFArFU6/XIzqdDvH3929NTk7uFihz5MiR3QakvLw8Sm5uLvWLL77gmo4ZjUZQqVSYyspK/NWrVykYDAamTJnS54tuzsKFC+WBgYGOIpHIdfTo0YopU6a0zZo1qw2LfdiB7+7duyQMBgPjxo3rGpDZbLbe0dGx01x7JBAIRvOI1/b29lqtVos0NjZiFQoF1t3dfajp3IoVK2p37NhRBwDg6OjYaRKAAABXr16lGI1GME8P8Kem+cYbb7T/VSeyj4+P0iQAe1JdXY1bs2YN78qVK3S5XI7T6/WIWq3GVFRU9GuefVxKSkpI8+fP7ybcRo0apczMzOzmrefj49OvgLl16xZJo9Egb7/9dq9aQ28cOnSIuW/fPhuZTEbq6OjAGAwGxNw5IiIiomHNmjX8ixcvWowePVoxa9as1jfffLMDAOC9995rTk5OtuHz+W5jxoxpmzRpkmLOnDmt5vfhWaBSqTAkEqmbANHpdBAbG8s9deoUq6GhgaDRaBCtVosMHz6827MrlUq79dn169cpKpUKY2Nj42F+XKPRYEpLS0mPU3ZPniay/JOSkpLCPHHiBPvAgQOlHh4equvXr5M3bNjAF4lEmqioqIcmTIcPH7YOCAhotbOze2ihkUKhGI1GI3R0dGCedK36deeVEIL/FHx8fNpTUlJkBALBKBAItEQi8aGBp2eUaKPRiERHR9fMmzevpWdaOzs7bc9jj8LPz6+jrKzs7unTpy0yMzPpERERwqSkpM7s7OzC3gRhXyDI/6y4WCzW2Ns5g8GACIVCjfls26RVAQBQKJRubdXr9YAgCGRnZ98nEAjdyuxvRm7OnDlzRE1NTbgdO3ZUSiQSNYlEMk6YMMFRo9E8l/Vv834BAKDRaM90YLp48SJ10aJFg6OiomoCAwMrWSyW/vvvv7fcsmVLl4NVVFRU07Rp09pOnTplkZWVxfD393detmxZ3e7du2skEom2uLj4XkZGBuPChQuM2NhY+4SEBLubN2/eZzAYz6yuLBZL19LS0m18iYuL4x44cIC7ffv2Ck9Pz04Gg2FYu3Ytr6mpqZsK0/O5MBgMCJvN1v78888FPa/DZDL1j1N2T57GHGpnZ6fV6/VQV1eHMxdQTU1NeF9fX2VveQAANm/ePCgyMrIuPDy8BQBg+PDhnTKZjPjZZ59xewrBK1eukPPy8ihbt26t7q2spqYmLJFINKIC8MlBheBzhEwmGx535uni4tJRUFBA6ivf8OHDOwwGA5w7d45uMoc+CiaTaQgLC2sJCwtrycrKkvv7+zvn5eURzc2KAABSqVRlMBggKyuLajKHNjc3YwoLC8nvvffegLz+8Hj8gGfbI0aM6DAajVBdXY2fOnVqrzN4qVTamZaWxlKpVEhv2uDNmzdp27dvrwgJCWkDAKisrMQ1NjY+czuRWCxWXblyhWY+aOXk5NDEYnFnf/l64uHhoSIQCMYffviB4ebm1qs5zJzLly/TbGxsNOYm0cTExIe0XLFYrF29enXT6tWrm2JjY7kHDx602b17dw3An9pDSEhIW0hISFtlZWUtn893z8zMpJmb4Z8WV1fXjoKCgm5rzb/99hvN39+/NTIyshngT4eg0tJSEoPB6Het1sfHpyMhIQGPwWCMfZn6n7TspzGHjho1qgOHwxkzMjIYS5YsaQYAKCkpwZeWlpL8/Pz6FIIqlQrTc+KIxWKNBoPhIU/YL7/80prH42nM1x3NuXfvHtnFxeWZm7NfJ1Ah+JITGxtb8+6770pWrlypmTdvXjMOh4Pbt2+Tr169Sj1w4ECVVCpVT548uSUyMlKoVCor3njjjY7y8nJCSUkJwTQgmPPRRx9xbG1ttcOGDesgEAjGI0eOsGg0ml4kEj2kVbq5uan/GlgEX375pYzFYuljYmJ4VCrVsGjRoqdyfe8NqVSqnjZtWvPixYuFTU1NVSNGjHjQ1NSEy8zMpIvFYvUHH3zQGh0d3XDkyBHrwMDAwZs2bapls9n6nJwcqpubW+fIkSM7hUKh6rvvvmP7+fk9UCqVmLVr19rj8fhnHjk6KiqqLiwsbHBCQsKDwMBARUZGhkV6ejorNTW15HHKYTKZhgULFtTHx8fziESiISAgQNnQ0IC9du0add26dQ8JRScnJ1VDQwNh//79rDFjxijPnDljkZ6e3s07MCwsbNCUKVPaXFxcVK2trdjMzEyGRCJRAfzp7arT6RA/P78HDAZDn5qaysLhcEYXF5c+PT/v3btHVCgUmJqaGrxWq0WuXLlCBgDw8vJS9WWWnjRpUltkZKTQfK1KLBarMjIyWD/99BPNxsZGt3v3bpvq6moCg8Hod+Iwffp0haenp3LGjBmS+Pj4KqlUqqqqqsKfO3fOYuLEiYpJkyYpn7TspzGHstls/ezZs5vi4uLsuVyu1traWhcdHT3I0dGx01xoiUSioYsWLWowrR/7+/u3JiUl2Q4ePFjj6enZefXqVcqBAwc477zzTrd3qr29HZOens5aunRpXV9r67/99hvd39+/rdeTKAMCFYIvOcHBwYrjx48Xb9u2zfarr77iYLFYEAqFqrlz53ZpIN9//31ZVFQUb+3atfzW1lYch8PRRERE1PdWHp1O1yclJXFkMhkJQRAYMmRIx8mTJ4t6mmFNfPvtt+WLFy8eNHv2bIlGo8F4eXkpz549W2hyUHjWHD9+vHz9+vW2mzZtsq+vr8dbWFjopVLpg/Hjx7cD/OlckZmZmb9q1Sr7t99+2wlBEHB0dOxMTk4uBwBISUkpX7x4sWDUqFEu1tbWmpiYmJo9e/Y8c01w/vz5rbW1tZX79u3jbNq0aZCdnZ1m586dFeZOMQNl37591UwmU79r1y671atX49lstm7WrFm9TjLmzp3blpOTU7dhw4ZBarUa4+fnp4iJiamJiYnp+qzFYDDAqlWr+HV1dQQKhaIfNWpU++eff14J8Kf5MDExkRsXF2ev0+kQsVisSk1NLTF3nunJggULhNevX6eZfps+GcnPz891cnLqNd/s2bPboqOjjWlpaRYmrXz79u21MpmMOHPmTAcikWiYPXu2fPr06c2FhYXk/voHg8FAZmZmUXR0NG/58uXC5uZmHJvN1vn4+CgXLlwof5qyn5aUlJTKJUuWGENDQ8VqtRrx9fVt//e//12Ew/1vaC0vLyeZm2VTUlIqVq1axYuOjuY3NzfjraystPPmzWv65JNPunl/fv3118zOzk5sREREr89CWVkZ/veD63BBAAAgAElEQVTff6d+++23pX9bA18DEKPxbxnL/jbu3LlT7u7u3qe3HQoKysvBJ598Yn3q1Cnmb7/9Vvii6/IqsnjxYnuFQoH99ttvZf2lu3PnjpW7u7vwOVXrHweqCaKgoPwtREdHNzY3N2Plcjn2Vd467UVhY2Oj3bhx42u1hd/fAaoJoqCgoLzCoJpg/6DbpqGgoKCgvLagQhAFBQUF5bUFFYIoKCgoKK8tqBBEQUFBQXltQYUgCgoKCsprCyoEUVBQUFBeW1AhiIKCgoLy2oIKwVecnpHlH0XPyPKvIjwez23z5s2cvn73xqeffmpla2vrhsFgvKOjo+3+/lr+DwRBvA8dOsR8dMqXi9zcXCKbzXaXy+UDD0+CMiA6OzsRW1tbt4G+1yh9gwrB50RwcLAQQRBvBEG8cTicl729vVt4eLi9QqH4W++BWCzWyGSyO76+vgPaaf7MmTPFSUlJVY9O+frQ2NiIjYmJ4S9btqy+rKzsblxc3GuzS8dvv/1Gnjp1qojL5UpJJJKXUCh03bhxI8c8fmFfrF27lhcaGtrwKu8W09nZiXzwwQeDmEymO5lM9hw3bpykpKRkwHvVrl+/nosgiLf5xFOtViMRERE8R0dHFzKZ7GltbS2dOnWqqKioqCtaCJlMNi5btqx+3bp19r2XjDJQUCH4HPH19VXIZLI7+fn5uRs3bqw+cuSIdURERJ8PsVqtfii0yuOCw+GAz+frBhp1msPh6JlM5nOPTaZSqZ66rX8XRUVFBL1ejwQHB7cKBALtk8Zue5nb2BdXr16lsNlsXUpKStmtW7fubdiwofqzzz6zi42N5faXr7i4GH/hwgXL3qKhPw4ve58tXLhw0A8//MD8+uuvyy5cuFCgVCqxU6ZMcdDpHop/+xAXL16kpqamWjs6OnaLdKFUKjF37tyhrFmzpva///3vH8ePHy+uqakhTJo0yUGr/V+wl4ULF8pv3rxJu3HjBumhwlEGDCoEnyNEItHI5/N1EolEu2TJkubp06c3/+c//7EEADh79iwdQRDvY8eOWbi5uQ3B4/FeJ0+eZAAAHD161GLo0KFDiESiF4/Hc1u+fDnPfHBQqVTIsmXLeHZ2dm4EAsHL3t7eLT4+3gbgYXOoWq1GQkNDB9nY2EgJBIIXl8uVLl26lGcqq6c5tLGxETtz5kwhg8HwIJFIXiNHjnQ0f+n27t3LplAonunp6XQHB4ehZDLZc8SIEY75+fn9RnIPDg4Wjh07VhIbG8vlcDhSOzs7qaktERERPA6HIyWTyZ6urq5D0tLSGOZ5f//9d9K4ceMkdDrdg0KheHp4eDhfu3aNDADwyy+/UEaNGuXAZDLdaTSap7e3t1NmZib1Se/Z3r172aaoCUOGDHFDEMS7oKCAAACwa9cuKz6f74rH4734fL5rYmKilXleBEG8ExISrCdMmCAmk8meK1as4PV2DYPBAHFxcRyBQOBKIBC8OByONDIyste0AABLly7lCYVCVxKJ5MXj8dyWLFli39HR0fU8FBcX4/39/cUWFhYeZDLZUyQSDU1OTu4yp65evdrW9KxYWVm5BwUFCfu61sqVK+WHDx+uDAwMbHdxcdGEh4e3zJ8/v/HMmTP9mmdTU1NZDg4OKgcHh64oE3V1ddipU6eKOByOlEQieUkkkqFJSUls83zDhw93mjdvHj88PNyeyWS6+/j4OAMAyOVy7Jw5cwQsFsudSqV6Dhs2zMncFDiQsp81crkce/z4cauPP/64KigoSOHn59dx9OjR0sLCQnJ6ejrjUXlDQ0NFBw4cKLewsOimKbPZbP2VK1eKFi1a1OLu7q4eO3ZsR3Jysqy0tJT0+++/d0XF4HA4ei8vL2Vqairr4SugDJRXYwPtRMT7hVx3lfHm02QnkUgGnU7Xbaa7adMm3o4dO6qcnZ1VlpaWhrS0NEZ4ePjgbdu2VQQEBChLS0sJy5cvF6jVaiQ5ObkKAGDWrFnCa9eu0Xfu3FkxfPjwjpKSEqJMJutVCG3fvt3mxx9/ZH7zzTelEolEU15ejv/jjz/6nEmGhIQIS0tLSceOHStms9n6mJgYXmBgoGNxcXGuKZySRqNBdu7caZucnFxGoVCMoaGhooULFwqys7N7jcht4tq1a3Q6na4/e/ZsodFoRAAAZs+eLZTJZMTDhw+XCgQCzenTpy1CQkIkly9fvu/r69tZXl6OHzdunJOXl5cyIyOjkM1m67Ozs6mmmXdbWxt27ty5cl9f30oEQeCzzz6zmTlzpkNhYWEul8t9bLPcggULmnk8nvadd95xuHTp0n2RSKSxs7PTpaamWq5fv56/ZcuWysDAQMWZM2cs1q1bx7e1tdWah1PatWuX3caNG6v37NlT2VdMuOXLl/NSU1Ott27dWhkQEKCsq6vD3bhxo8+1HiqVakhOTi4TCATa27dvk1euXMknEomGpKSkGgCA8PBwgUqlQn788ccCJpOpv3fvXtf9PXz4sOWBAwe4KSkppd7e3p21tbW47Ozsx5okKBQKTM+Buyc5OTk0d3f3B+bHOjs7MR4eHh3r16+vs7S01J8/f56xevVqgVAo1EyfPr0riPLp06fZc+fObczKyiowGo1gMBhgwoQJEjqdrk9LSyuytrbWHzx4kD158mSnvLy8ewKBQDvQsnvyNJHlc3JyKDqdDjGPHSiRSLSDBw9WZWdn04KDg/sMUvz+++8LAgMDW6ZOndq+devW/qoAAACtra1YAAA2m91NxfTy8nqQk5NDf2QBKH3yagjBfyA///wzJT09nTVy5MhuL+iGDRtqzCN8JyQk2EZERNR9+OGHcgCAoUOHqltbW6sWL14sOnDgQFVeXh7x7NmzrBMnThSZIsv3FX0bAEAmkxFEIpFq4sSJSgwGAw4ODprx48c/6C1tbm4uMSsry/L8+fMFpsjyJ06cKBMKhW7Jycns6OjoJgAAvV6P7N+/X+bu7q4GAFixYkXdhx9+KDQYDNDXwA8AQCAQDMeOHSsnk8lGAABTWwoKCnJNGoSLi0tjVlYW44svvrD29fWtSExMtCGTyYZz586VmgK6SqXSrsCo06ZN69afhw8fruBwOJYnT560WLp06UNBhh8FjUYzWltb6wAAuFyujs/n6wAA9uzZww0KCmo2BUqVSqUNt27doiQmJnLNheDUqVObTf3UG21tbZiUlBTO1q1bK1euXCkH+DPQa0BAQK/3BADAPKq8k5OTRiaT1e3bt49jEoJVVVWEqVOntvj6+nYCAJjHCiwvLydaWVlpg4KCFEQi0ejg4KAZPXr0gCOTZ2dnU9LS0qy++uqrfmPYVVdXE6VSaTczn0gk0m7durUrzqWLi0vTpUuXGEePHmWZCyoej6c+ePBg17p0RkYG/f79+5SGhobbpolXUlJSzU8//WR58OBBVnx8fP1Ay+7J00SWr6mpwWOxWOByud0Ek5WVlba+vr7P9YfExESr8vJy4smTJ8v6u7YJlUqFrFmzZtDYsWPbxGJxt+DXdnZ22urqauJAykHpnVdDCD6lRva8+PXXXy0oFIqnXq9HdDod4u/v35qcnFxhnmbkyJHdBqS8vDxKbm4u9YsvvuhagzEajaBSqTCVlZX4q1evUjAYDEyZMqXPF92chQsXygMDAx1FIpHr6NGjFVOmTGmbNWtWGxb7sAPf3bt3SRgMBsaNG9c1ILPZbL2jo2OnufZIIBCMJgEIAGBvb6/VarVIY2MjVqFQYN3d3Yeazq1YsaJ2x44ddQAAjo6OnSYBCPDn+pPRaATz9AB/appvvPFG+191Ivv4+Cj7imheXV2NW7NmDe/KlSt0uVyO0+v1iFqtxlRUVPRrnn1cSkpKSPPnz+8m3EaNGqXMzMy0ND/m4+PTr4C5desWSaPRIG+//XafWkNPDh06xNy3b5+NTCYjdXR0YAwGA2LuqBIREdGwZs0a/sWLFy1Gjx6tmDVrVuubb77ZAQDw3nvvNScnJ9vw+Xy3MWPGtE2aNEkxZ86cVvP70Bd37twhBgUFSf7v//6vPjQ0tLW/tCqVCkMikboJEJ1OB7GxsdxTp06xGhoaCBqNBtFqtcjw4cO7PbtSqbRbn12/fp2iUqkwNjY2HubHNRoNprS0lPQ4ZffkaSLLPwl37twhxsfH8y5dupRPJBIf2edarRZmzpwpUigU2PPnzz+kkZLJZMOz8B14nXk1hOA/BB8fn/aUlBQZgUAwCgQCbW8vQc8I70ajEYmOjq6ZN29eS8+0dnZ22p7HHoWfn19HWVnZ3dOnT1tkZmbSIyIihElJSZ3Z2dmFvQnCvkCQ/713WCzW2Ns5g8GACIVCjfls26RVAQBQKJRubdXr9YAgCGRnZ98nEAjdyuxvRm7OnDlzRE1NTbgdO3ZUSiQSNYlEMk6YMMFRo9E8l/Vv834BAKDRaM/UyejixYvURYsWDY6KiqoJDAysZLFY+u+//95yy5YtXQ5WUVFRTdOmTWs7deqURVZWFsPf39952bJldbt3766RSCTa4uLiexkZGYwLFy4wYmNj7RMSEuxu3rx5n8Fg9FnX33//nTRhwgTHadOmtXz55ZfVj6oni8XStbS0dBtf4uLiuAcOHOBu3769wtPTs5PBYBjWrl3LM4+6DvDwc2EwGBA2m639+eefC3peh8lk6h+n7J48jTnUzs5Oq9froa6uDmdnZ9f1XDc1NeF9fX2VveX55ZdfaK2trThvb29X0zG9Xg83btygHT161Lq9vf2WaUKi1Wph2rRpg/Pz88m//PJLQW/m/ObmZhyTyXy0Fw5Kn6BC8DlCJpMNjzvzdHFx6SgoKCD1lW/48OEdBoMBzp07RzeZQx8Fk8k0hIWFtYSFhbVkZWXJ/f39nfPy8ojmZkUAAKlUqjIYDJCVlUU1mUObm5sxhYWF5Pfee29AXn94PH7As+0RI0Z0GI1GqK6uxk+dOrXXGbxUKu1MS0tjqVQqpDdt8ObNm7Tt27dXhISEtAEAVFZW4hobGwfssj5QxGKx6sqVK7SoqKgubTAnJ4cmFos7+8vXEw8PDxWBQDD+8MMPDDc3t8ZHpb98+TLNxsZGY24STUxMfEjLFYvF2tWrVzetXr26KTY2lnvw4EGb3bt31wAAUCgUY0hISFtISEhbZWVlLZ/Pd8/MzKSZm+HNuXnzJmnixIlOgYGBzf/6178qB9IuV1fXjoKCgm5rzb/99hvN39+/NTIyshngT4eg0tJSEoPB6Hd90cfHpyMhIQGPwWCMfZn6n7TspzGHjho1qgOHwxkzMjIYS5YsaQYAKCkpwZeWlpL8/Px6FYJz585t9fX1zTM/FhYWJhKJRKrNmzfXmibGarUamTZt2uCCggLyL7/8UmAyw/ckLy+P7OrqOmBzNsrDoELwJSc2Nrbm3XfflaxcuVIzb968ZhwOB7dv3yZfvXqVeuDAgSqpVKqePHlyS2RkpFCpVFa88cYbHeXl5YSSkhKCaUAw56OPPuLY2tpqhw0b1kEgEIxHjhxh0Wg0vUgkekirdHNzU/81sAi+/PJLGYvF0sfExPCoVKph0aJFT+X63htSqVQ9bdq05sWLFwubmpqqRowY8aCpqQmXmZlJF4vF6g8++KA1Ojq64ciRI9aBgYGDN23aVMtms/U5OTlUNze3zpEjR3YKhULVd999x/bz83ugVCoxa9eutcfj8c88cnRUVFRdWFjY4ISEhAeBgYGKjIwMi/T0dFZqamrJ45TDZDINCxYsqI+Pj+cRiURDQECAsqGhAXvt2jXqunXrHhKKTk5OqoaGBsL+/ftZY8aMUZ45c8YiPT29m3dgWFjYoClTprS5uLioWltbsZmZmQyJRKIC+NPbVafTIX5+fg8YDIY+NTWVhcPhjC4uLqre6nfjxg3SxIkTnXx9fdu3bNlSW1FR0TVm9DUwAwBMmjSpLTIyUqjVasH0eY5YLFZlZGSwfvrpJ5qNjY1u9+7dNtXV1QQGg9HvxGH69OkKT09P5YwZMyTx8fFVUqlUVVVVhT937pzFxIkTFZMmTVI+adlPYw5ls9n62bNnN8XFxdlzuVyttbW1Ljo6epCjo2OnubOMSCQaumjRooYNGzb8//buPKypK20A+LlJyAqBBAiBQEgggAJJCCCILH0URrGADFAZFKzYT1AUXKhFBC2tOOAy2NLRSpFRBp9qXVBBrO0Y6SLouFeBKjthE9kEBBKyfn84oQEBca3K+T0Pf5DcLTfJfc95z8l9OwwMDBQGBgYjAjORSFRSKBTFjBkzJAA87gH6+flZ3Llzh3Ty5MkaBEFU6vNOpVIV6nFRAAC4du2adlJS0lN75tD44E8k3nAhISF9x44dq7l48aKOl5fXdE9Pz+kZGRl0JpM5/OU9ceJEfVBQUHdCQgKTx+PZR0VFsXp7e8fMbero6CgyMzONPD09p8+cOdO2vLycePLkyerRaVi1I0eONDg4OAyEhoZyvLy8pkskElRRUVGV5hfxZTp27FhDWFhY15YtW0z5fL59cHCwVUlJiY6FhYUUgMeTK4RC4T2ZTIbMnz/fxtXV1TYrK4umDnQ5OTkNAwMDKHd3d9uIiAiLDz/8sJPBYIw7Ueh5LVmypCctLa1p3759RgKBwO6bb76h7dixo1FzUsxk7dmzpyU2NrZt165dJg4ODnaLFi3iNDc3jzmGuXjx4t4VK1a0JSUlmTk7O9tduHCBnJiY2Kq5jFKpBB9//DFTIBDY+/n5WRsaGsq//fbbegAepw/z8vIMvL29bQQCgV1hYSElLy+vVnPyjKbDhw9Tu7u7MWfPnqWYm5vzNf8mek2hoaG9OBxOlZ+fr6t+LC0t7T6fzx8IDg628vb2tiGRSMrAwMCnTlZCoVBAKBRWu7u7P4qLi2NxuVz7iIgIy+rqaryZmZnsRbb9onJycpp8fX0fRkZGWvr4+EwjkUjKoqKiagzmj/5FQ0MD/mlpWU11dXXYCxcu6HV0dGh5enpO1zznBw4cGG7wCIVC0qNHj9BLly59YqgEmjxEpXol17JX5vbt2w18Pn/c2XYQBL0Zdu7caXjq1CnK5cuXq/7sY3kXzZ8/34LP5w+qJ5qN5/bt2wZ8Pp/1mg7rrQPToRAEvRLx8fEd3d3d6K6uLvS7fOu0P4NYLEbs7e3FmzdvfvD0paGJwJ4gBEHQOwz2BCcGxwQhCIKgKQsGQQiCIGjKgkEQgiAImrJgEIQgCIKmLBgEIQiCoCkLBkEIgiBoyoJBEIIgCJqyYBB8x42uLP80oyvLv4sYDAb3008/NRrv/7H84x//MDA2NuaiUCin+Ph4k1d/lH9AEMTp4MGDE1ZyfxOVlZXh9PX1+V1dXZMvTwJNGpfLnZ6bm6v39CWhicAg+JqEhISwEARxQhDECYPBOJqamnKjo6NN+/r6Xul7YGlpKRWJRLfd3Nwmdaf5M2fO1GRmZjY/fcmpo6OjA52YmMiMjY19UF9ffyclJWXC21S9S1pbWzEeHh5WNBqNh8ViHel0Om/JkiXMyQS2hIQERmRkZPu7fLcYsViMLF261IxCofAJBIJgzpw5nNra2gnvEyqXy8HatWtNGAwGF4fDOTIYDO6aNWtMZLI/7mGvvlaM/luyZMlwA3XTpk2tKSkpppq1JKFnB4Pga+Tm5tYnEolu37t3r2zz5s0thw4dMoyJiTEdb/mXUSwTg8EAJpMpV9/J/2mMjIwUFArlpdbAmwyJRPLGFgatrq7GKhQKJCQkpMfc3Fymq6v7XOfnTX6N40GhUKoFCxb05Ofn15SXl5d/88039SUlJTpLliwxn2i9mpoarfPnz+utXLnyhaqNvOnnbPny5Wbnzp2jHDhwoP78+fOV/f39aD8/Pyu5fPwSf5s3b6bn5ubSdu3a1Xjnzp3ynTt3Nubm5tKSkpKM1cuIRKLbmn+HDx+uAQCAsLCw4ZuCh4aG9g4MDKCPHz+uO9Z+oMmBQfA1wuFwKiaTKedwOLKVK1d2BwYGdv/nP//RAwCAoqIiHQRBnI4eParL5XKna2lpOZ48eZIMAACHDx/WtbOzm65uNcbFxTE0Lw4SiQSJjY1lmJiYcLFYrKOpqSl327ZtNACeTIcODQ0hkZGRZpot+1WrVjHU2xqdDu3o6EAHBwezyGSyAx6Pd5w1a5b19evXh+vEffXVV/pEIlFQUFCgY2VlZUcgEASurq7W9+7dm7CSe0hICGv27Nmc5ORkupGREc/ExISnfi0xMTEMIyMjHoFAENjb20/Pz88na65769Yt/Jw5czg6OjoORCJR4ODgMO3q1asEAAD45ZdfiO7u7lYUCoWvra0tcHJyshEKhaTnfc+++uorfXd3d1sAAJg+fToXQRCnyspKLAAA7Nq1y4DJZNpraWk5MplM+4yMDAPNdREEcUpPTzecO3euJYFAEKxZs4Yx1j6USiVISUkxMjc3t8disY5GRka81atXj7ksAACsWrWKwWKx7PF4vCODweCuXLnSdHBwcPjzUFNTo+Xt7W2pq6vrQCAQBGw22y47O3s4nbphwwZj9WfFwMCAHxQUxBpvX3Q6XZGQkNDh6ek5aG1tLQ0MDHz0f//3fx3Xrl3Tnui85eXlUa2srCRWVlbD1Sna2trQAQEBbCMjIx4ej3fkcDh2mZmZ+prrubi42ISHhzOjo6NNKRQK39nZeRoAAHR1daEXLVpkTqVS+SQSSTBjxgwbzRT/ZLb9snV1daGPHTtm8PnnnzcHBQX1eXh4DB4+fLiuqqqKUFBQQB5vvStXrmh7e3v3LF68uNfGxkYaHh7e6+Pj03P9+vXhzymTyZRr/hUUFOiZm5sP+fn5DdcpxGAwYPbs2b1Hjhyhjr0naDLeiRtoR33d7fRn7Hf/KuqNF1kfj8cr5XL5iJbuli1bGNu3b2+eNm2aRE9PT5mfn0+Ojo62+Pvf/97o4+PTX1dXh42LizMfGhpCsrOzmwEAYOHChayrV6/q7Nixo9HFxWWwtrYWJxKJxgxCaWlptB9++IHy73//u47D4UgbGhq0fv/9d/xYywIAQFhYGKuurg5/9OjRGn19fUViYiLD39/fuqampkxdTkkqlSI7duwwzs7OricSiarIyEj28uXLzUtKSsasyK129epVHR0dHUVRUVGVSqVCAAAgNDSUJRKJcLm5uXXm5ubS06dP64aFhXF+/fXXu25ubuKGhgatOXPm2Dg6OvYXFhZW6evrK0pKSkjqlndvby968eLFXW5ubk0IgoAvvviCFhwcbFVVVVU2VmXup/noo4+6GQyG7IMPPrD6+eef77LZbKmJiYk8Ly9Pb9OmTcytW7c2+fv79505c0Z348aNTGNjY5lmOaVdu3aZbN68ueXLL79sQqHGbnPGxcUx8vLyDFNTU5t8fHz629raMNevXx93DJdEIimzs7Przc3NZb/99hth3bp1TBwOp8zMzGwFAIDo6GhziUSC/PDDD5UUCkVRXl4+/P7m5ubqZWVl0XNycuqcnJzE9+/fx5SUlEy6kdDQ0KBVWFhIcXV1HbNorFppaak2n88f0HxMLBajHBwcBjdt2tSmp6en+P7778kbNmwwZ7FY0sDAwOEiyqdPn9ZfvHhxR3FxcaVKpQJKpRLMnTuXo6Ojo8jPz682NDRU7N+/X//999+3qaioKDc3N5dNdtujvUhl+dLSUqJcLkc0awdyOByZhYWFpKSkRDskJGTMIsVubm79Bw8eNLx16xZeIBBIbty4gb906RJ53bp198davre3F3XmzBnqxx9/3Dr6uRkzZgzs3r3beKz1oMl5J4Lg2+inn34iFhQUUGfNmjXiC5qUlNSqWeE7PT3dOCYmpm3t2rVdAABgZ2c31NPT07xixQp2VlZWc0VFBa6oqIh6/PjxanVl+fGqbwMAgEgkwrLZbMm8efP6USgUsLKykv7lL38ZGGvZsrIyXHFxsd73339fqa4sf/z48XoWi8XNzs7Wj4+P7wQAAIVCgezbt0/E5/OHAABgzZo1bWvXrmUplUow3oUfAACwWKzy6NGjDQQCQQUAAOrXUllZWabuQdja2nYUFxeT9+7da+jm5taYkZFBIxAIyrNnz9apK8vzeLzh2ooLFiwYcT5zc3MbjYyM9E6ePKm7atWqZ64vp62trTI0NJQDAACdTperC8l++eWX9KCgoO6kpKSO/x1D+82bN4kZGRl0zSAYEBDQrT5PY+nt7UXl5OQYpaamNq1bt64LgMeFXn18fMZ8TwAAQLOqvI2NjVQkErXt2bPHSB0Em5ubsQEBAQ/d3NzEAACgWSuwoaEBZ2BgIAsKCurD4XAqKysrqZeX11PHiwMCAthCoVBPIpGgZs+e3fvdd9/VT7R8S0sLjsfjjShoy2azZampqcNVD2xtbTt//vln8uHDh6magYrBYAzt379/eFy6sLBQ5+7du8T29vbf1A2vzMzM1h9//FFv//791G3btj2Y7LZHe5HK8q2trVpoNBrQ6fQRuU8DAwPZgwcPxh1/2LZtW9ujR4/QTk5OdigUSqVQKJC4uLj7iYmJTxRRBgCA/fv3U2UyGTJWapnBYEjb29u1NIsXQ8/mnQiCL9oje10uXryoSyQSBQqFApHL5Yi3t3dPdnZ2o+Yys2bNGnFBqqioIJaVlZH27t1LVz+mUqmARCJBNTU1aV25coWIQqGAn5/fuF90TcuXL+/y9/e3ZrPZ9l5eXn1+fn69Cxcu7EWjn5zncOfOHTwKhQJz5swZviDr6+srrK2txZq9RywWq1IHQAAAMDU1lclkMqSjowPd19eH5vP5durn1qxZc19d/8za2lqsDoAAAHDlyhWiSqUCmssD8LinOXPmzEf/OyaCs7NzvzoAjtbS0oL55JNPGJcuXdLp6urCKBQKZGhoCNXY2DhhevZZ1dbW4pcsWTIiuLm7u/cLhcIRs/WcnZ0nDDA3b97ES6VSZP78+WP2GsZy8OBByp49e2gikQg/ODiIUiqViObkiJiYmPZPPvmEeeHCBV0vL6++hQsX9nh6eg4CALy5y1QAAB2zSURBVEBERER3dnY2jclkct97771eX1/fvkWLFvVovg9j2bt3b1NXV9f933//HZeSkmIaHR3NPHz4sGi85SUSCQqPx48IIHK5HCQnJ9NPnTpFbW9vx0qlUkQmkyEuLi4jPrs8Hm/EObt27RpRIpGgaDSag+bjUqkUVVdXh3+WbY/2IpXln1dOTg7l+PHj+llZWXUODg6Sa9euEZKSkphsNlu6fv36JxpMubm5hj4+Pj0mJiZPDDQSiUSVSqUCg4ODqOcdq57q3okg+LZwdnZ+lJOTI8JisSpzc3MZDod74sIzusK7SqVC4uPjW8PDw5+oHm1iYiIb/djTeHh4DNbX1985ffq0rlAo1ImJiWFlZmaKS0pKqsYKhONBkD+yuGg0WjXWc0qlEmGxWFLN1ra6VwUAAEQiccRrVSgUAEEQUFJScheLxY7Y5kQtck2LFi1id3Z2YrZv397E4XCG8Hi8au7cudZSqfS1jH9rnhcAANDW1n6pF6YLFy6QoqKiLNavX9/q7+/fRKVSFSdOnNDbunXr8ASr9evXdy5YsKD31KlTusXFxWRvb+9psbGxbbt3727lcDiympqa8sLCQvL58+fJycnJpunp6SY3bty4SyaTxz1W9diUQCCQGBgYKHx9fW22bt3ayuFwxvwMUqlU+cOHD0dcX1JSUuhZWVn0tLS0RoFAICaTycqEhATG6Krroz8XSqUS0dfXl/3000+Vo/dDoVAUz7Lt0V4kHWpiYiJTKBSgra0NoxmgOjs7tdzc3MZNF3/66admq1evbouOjn4IAAAuLi5ikUiE++KLL+ijg+ClS5cIFRUVxNTU1JaxttXZ2YnG4XAqGACfHwyCrxGBQFA+a8vT1tZ2sLKyEj/eei4uLoNKpRKcPXtWR50OfRoKhaJctmzZw2XLlj0sLi7u8vb2nlZRUYHTTCsCAACPx5MolUpQXFxMUqdDu7u7UVVVVYSIiIhJzfrT0tKadGvb1dV1UKVSgZaWFq2AgIAxW/A8Hk+cn59PlUgkyFi9wRs3bminpaU1hoWF9QIAQFNTE6ajo+Ol54ksLS0lly5d0ta8aJWWlmpbWlqKJ1pvNAcHBwkWi1WdO3eOzOVyx0yHafr111+1aTSaVDMlmpGR8UQv19LSUrZhw4bODRs2dCYnJ9P3799P2717dysAj3sPYWFhvWFhYb1NTU33mUwmXygUamum4SeiVD6+3kokknEbFvb29oOVlZUjxpovX76s7e3t3bN69epu9Xbq6urwZDJ5wrFaZ2fnwfT0dC0UCqUaL9X/vNt+kXSou7v7IAaDURUWFpJXrlzZDQAAtbW1WnV1dXgPD49xg6BEIkGNbjii0WiVUql8Yibs119/bchgMKSa446aysvLCba2tpP6+RM0NhgE33DJycmtf/vb3zjr1q2ThoeHd2MwGPDbb78Rrly5QsrKymrm8XhD77///sPVq1ez+vv7G2fOnDnY0NCAra2txaovCJo+++wzI2NjY9mMGTMGsVis6tChQ1RtbW0Fm81+okXP5XKH/ndhMf/6669FVCpVkZiYyCCRSMqoqKgXmvo+Fh6PN7RgwYLuFStWsDo7O5tdXV0HOjs7MUKhUMfS0nJo6dKlPfHx8e2HDh0y9Pf3t9iyZct9fX19RWlpKYnL5YpnzZolZrFYku+++07fw8NjoL+/H5WQkGCqpaX10itHr1+/vm3ZsmUW6enpA/7+/n2FhYW6BQUF1Ly8vNpn2Q6FQlF+9NFHD7Zt28bA4XBKHx+f/vb2dvTVq1dJGzdufCIo2tjYSNrb27H79u2jvvfee/1nzpzRLSgoGDE7cNmyZWZ+fn69tra2kp6eHrRQKCRzOBwJAI9nu8rlcsTDw2OATCYr8vLyqBgMRmVraysZ6/iOHDmi29nZiZk5c+YAmUxW3rp1i5CcnGzK5/MHJmrc+Pr69q5evZqlOVZlaWkpKSwspP7444/aNBpNvnv3blpLSwuWTCZP2HAIDAzsEwgE/X/9618527Zta+bxeJLm5mats2fP6s6bN6/P19e3/3m3/SLpUH19fUVoaGhnSkqKKZ1OlxkaGsrj4+PNrK2txZpBi81m20VFRbWrx4+9vb17MjMzjS0sLKQCgUB85coVYlZWltEHH3ww4jv16NEjVEFBAXXVqlVt442tX758Wcfb27t3zCehSYE/kXjDhYSE9B07dqzm4sWLOl5eXtM9PT2nZ2Rk0JlM5vCX98SJE/VBQUHdCQkJTB6PZx8VFcXq7e0dM7epo6OjyMzMNPL09Jw+c+ZM2/LycuLJkyerR6dh1Y4cOdLg4OAwEBoayvHy8poukUhQRUVFVeoJCi/bsWPHGsLCwrq2bNliyufz7YODg61KSkp0LCwspAA8nlwhFArvyWQyZP78+Taurq62WVlZNHWgy8nJaRgYGEC5u7vbRkREWHz44YedDAZj3IlCz2vJkiU9aWlpTfv27TMSCAR233zzDW3Hjh2NmpNiJmvPnj0tsbGxbbt27TJxcHCwW7RoEae5uXnMMczFixf3rlixoi0pKcnM2dnZ7sKFC+TExMQRswaVSiX4+OOPmQKBwN7Pz8/a0NBQ/u2339YD8Dh9mJeXZ+Dt7W0jEAjsCgsLKXl5ebWak2c0EQgE5b/+9S/DOXPmTOPz+faJiYlmc+fO7Tl//vyEM39DQ0N7cTicKj8/f/g3bGlpaff5fP5AcHCwlbe3tw2JRFIGBgY+dbISCoUCQqGw2t3d/VFcXByLy+XaR0REWFZXV+PNzMxkL7LtF5WTk9Pk6+v7MDIy0tLHx2caiURSFhUVVWMwf/QvGhoa8Jpp2ZycnEY/P7+H8fHxTD6fb5+UlGQWHh7emZmZOSLleeDAAYpYLEbHxMSM2eCsr6/XunXrFmnlypXjTryCng5RqV7JteyVuX37dgOfz4dvOgS94Xbu3Gl46tQpyuXLl6v+7GN5F61YscK0r68PfeTIkXEnKAEAwO3btw34fD7rNR3WWwemQyEIeiXi4+M7uru70V1dXeh3+dZpfxYajSbbvHnzlLmF36sCe4IQBEHvMNgTnBgcE4QgCIKmLBgEIQiCoCkLBkEIgiBoyoJBEIIgCJqyYBCEIAiCpiwYBCEIgqApCwZBCIIgaMqCQfAdN7qy/NOMriz/LmIwGNxPP/3UaLz/x/KPf/zDwNjYmItCoZzi4+NNXv1R/gFBEKeDBw9Snr7km6WsrAynr6/P7+rqmnx5EmhSxGIxYmxszJ3s9xoaHwyCr0lISAgLQRAnBEGcMBiMo6mpKTc6Otq0r6/vlb4HlpaWUpFIdNvNzW1Sd5o/c+ZMTWZmZvPTl5w6Ojo60ImJiczY2NgH9fX1d1JSUqbkXTru37+PodFoPARBnO7fv//Uu00lJCQwIiMj29/lu8WIxWJk6dKlZhQKhU8gEARz5szh1NbWTli1JD4+3kR9LVD/GRgY8DWXaWpqwoSEhLBoNBqPQCAIPD09rcrKynDq5wkEgio2NvbBxo0bTZ/cA/QsYBB8jdzc3PpEItHte/fulW3evLnl0KFDhjExMeN+iIeGhp4orfKsMBgMYDKZ8slWnTYyMlJQKJTXXptMIpG88Gt9Vaqrq7EKhQIJCQnpMTc3lz1v7bY3+TVORnh4OGuyZXtqamq0zp8/rzdWNfRn8aafs+XLl5udO3eOcuDAgfrz589X9vf3o/38/Kzk8ifq347AYrEkIpHotvrvzp07FernlEolCAgI4NTX1+OPHj1a89///vd3MzMz6bx586w1G83Lly/vunHjhvb169fxY+8FmgwYBF8jHA6nYjKZcg6HI1u5cmV3YGBg93/+8x89AAAoKirSQRDE6ejRo7pcLne6lpaW48mTJ8kAAHD48GFdOzu76TgczpHBYHDj4uIYmhcHiUSCxMbGMkxMTLhYLNbR1NSUu23bNhoAT6ZDh4aGkMjISDMajcbDYrGOdDqdt2rVKoZ6W6PToR0dHejg4GAWmUx2wOPxjrNmzbLW/NJ99dVX+kQiUVBQUKBjZWVlRyAQBK6urtb37t2bsJJ7SEgIa/bs2Zzk5GS6kZERz8TEhKd+LTExMQwjIyMegUAQ2NvbT8/Pzydrrnvr1i38nDlzODo6Og5EIlHg4OAw7erVqwQAAPjll1+I7u7uVhQKha+trS1wcnKyEQqFpOd9z7766it9d3d3WwAAmD59OhdBEKfKykosAADs2rXLgMlk2mtpaTkymUz7jIwMA811EQRxSk9PN5w7d64lgUAQrFmzhjHWPpRKJUhJSTEyNze3x2KxjkZGRrzVq1ePuSwAAKxatYrBYrHs8Xi8I4PB4K5cudJ0cHBw+PNQU1Oj5e3tbamrq+tAIBAEbDbbLjs7ezidumHDBmP1Z8XAwIAfFBTEetp5SE1NpYnFYlR8fPyDp540AEBeXh7VyspKYmVlNVydoq2tDR0QEMA2MjLi4fF4Rw6HY5eZmamvuZ6Li4tNeHg4Mzo62pRCofCdnZ2nAQBAV1cXetGiReZUKpVPIpEEM2bMsNFMBU5m2y9bV1cX+tixYwaff/55c1BQUJ+Hh8fg4cOH66qqqggFBQXkidZVN07Vf5pFecvLy3G3b98m7d27VzR79uxBPp8/dOjQIZFEIkHl5OQMl80yMjJSODo69ufl5VHH3gs0Ge/EDbSRzz93+jP2q0pJufEi6+PxeKVcLh/R0t2yZQtj+/btzdOmTZPo6ekp8/PzydHR0RZ///vfG318fPrr6uqwcXFx5kNDQ0h2dnYzAAAsXLiQdfXqVZ0dO3Y0uri4DNbW1uJEItGYQSgtLY32ww8/UP7973/XcTgcaUNDg9bvv/8+bksyLCyMVVdXhz969GiNvr6+IjExkeHv729dU1NTpi6nJJVKkR07dhhnZ2fXE4lEVWRkJHv58uXmJSUlE5bbuXr1qo6Ojo6iqKioSqVSIQAAEBoayhKJRLjc3Nw6c3Nz6enTp3XDwsI4v/766103NzdxQ0OD1pw5c2wcHR37CwsLq/T19RUlJSUkdcu7t7cXvXjx4i43N7cmBEHAF198QQsODraqqqoqo9Ppz5yW++ijj7oZDIbsgw8+sPr555/vstlsqYmJiTwvL09v06ZNzK1btzb5+/v3nTlzRnfjxo1MY2NjmWY5pV27dpls3ry55csvv2waryZcXFwcIy8vzzA1NbXJx8env62tDXP9+vVxx3pIJJIyOzu73tzcXPbbb78R1q1bx8ThcMrMzMxWAACIjo42l0gkyA8//FBJoVAU5eXlw+9vbm6uXlZWFj0nJ6fOyclJfP/+fUxJScmEjYTS0lLCP//5T/qVK1fuVlRUTKrXUVpaqs3n8wc0HxOLxSgHB4fBTZs2tenp6Sm+//578oYNG8xZLJY0MDBwuIjy6dOn9RcvXtxRXFxcqVKpgFKpBHPnzuXo6Ogo8vPzqw0NDRX79+/Xf//9920qKirKzc3NZZPd9mgvUlm+tLSUKJfLEc3agRwOR2ZhYSEpKSnRDgkJGbdIcXNzM5ZGo/G0tLRUAoFgYOfOnc3qgsHqYsVEInE444BGowEWi1WVlpZqx8fHD9872dHRcaC0tFRnotcATeydCIJvo59++olYUFBAnTVr1ogvaFJSUqtmhe/09HTjmJiYtrVr13YBAICdnd1QT09P84oVK9hZWVnNFRUVuKKiIurx48er1ZXlx6u+DQAAIpEIy2azJfPmzetHoVDAyspK+pe//GVgrGXLyspwxcXFet9//32lurL88ePH61ksFjc7O1tf/WVUKBTIvn37RHw+fwgAANasWdO2du1allKpBONd+AEAAIvFKo8ePdpAIBBUAACgfi2VlZVl6h6Era1tR3FxMXnv3r2Gbm5ujRkZGTQCgaA8e/ZsnbqyPI/HG66tuGDBghHnMzc3t9HIyEjv5MmTuqtWrXrm+nLa2toqQ0NDOQAA0Ol0OZPJlAMAwJdffkkPCgrqVhdK5fF47Tdv3iRmZGTQNYNgQEBAt+ZFa7Te3l5UTk6OUWpqatO6deu6AHhc6NXHx2fM9wQAADSrytvY2EhFIlHbnj17jNRBsLm5GRsQEPDQzc1NDAAAmrUCGxoacAYGBrKgoKA+HA6nsrKyknp5eY2b4uzr60OFh4db7ty5s5HNZssmGwRbWlpwPB5vREFbNpstS01NHe5J2tradv7888/kw4cPUzUDFYPBGNq/f//wuHRhYaHO3bt3ie3t7b+pG16ZmZmtP/74o97+/fup27ZtezDZbY/2IpXlW1tbtdBoNKDT6SNynwYGBrIHDx6MO/4wc+bMfltbWzGXy5Xcv38fk5aWZuLl5TW9vLy8nE6nK/h8vsTY2FiakJBgmpeX16Crq6tMTU01evDggdbo7ZqYmMhaWlpw4+0Lerp3Igi+aI/sdbl48aIukUgUKBQKRC6XI97e3j3Z2dmNmsvMmjVrxAWpoqKCWFZWRtq7dy9d/ZhKpQISiQTV1NSkdeXKFSIKhQJ+fn7jftE1LV++vMvf39+azWbbe3l59fn5+fUuXLiwF41+cgLfnTt38CgUCsyZM2f4gqyvr6+wtrYWa/YesVisSh0AAQDA1NRUJpPJkI6ODnRfXx+az+fbqZ9bs2bN/e3bt7cBAIC1tbVYHQABAODKlStElUoFNJcH4HFPc+bMmY/+d0wEZ2fnfnUAHK2lpQXzySefMC5duqTT1dWFUSgUyNDQEKqxsXHC9Oyzqq2txS9ZsmREcHN3d+8XCoV6mo85OztPOIZ28+ZNvFQqRebPnz9ur2G0gwcPUvbs2UMTiUT4wcFBlFKpRBSKPzq5MTEx7Z988gnzwoULul5eXn0LFy7s8fT0HAQAgIiIiO7s7Gwak8nkvvfee72+vr59ixYt6tF8HzQtX77czMXF5VFkZGTPZI8PgMe9GTwePyKAyOVykJycTD916hS1vb0dK5VKEZlMhri4uIz47PJ4vBHn7Nq1a0SJRIKi0WgOmo9LpVJUXV0d/lm2PdqLVJZ/XqGhoSPe69mzZ1ez2WxuVlaWwWefffYAh8Opjh49WhsVFcWi0+kOaDQauLm59Xl5efWqMyZqBAJB+TLmDkxl70QQfFs4Ozs/ysnJEWGxWJW5ubkMh8M9ceEZXeFdpVIh8fHxreHh4Q9HL2tiYiJ71mPw8PAYrK+vv3P69GldoVCoExMTw8rMzBSXlJRUjRUIx4Mgf3zv0Gi0aqznlEolwmKxpJqtbXWvCoCR6R4AAFAoFABBEFBSUnIXi8WO2OZELXJNixYtYnd2dmK2b9/exOFwhvB4vGru3LnWUqn0tYx/a54XAADQ1tZ+qZOMLly4QIqKirJYv359q7+/fxOVSlWcOHFCb+vWrcMTrNavX9+5YMGC3lOnTukWFxeTvb29p8XGxrbt3r27lcPhyGpqasoLCwvJ58+fJycnJ5ump6eb3Lhx4y6ZTH7iWEtLS8ltbW1YDAZjAMDjBhgAAJiZmfFjYmLa/vnPf7aMXgcAAKhUqvzhw4cjri8pKSn0rKwselpaWqNAIBCTyWRlQkICQ7PqOgBPfi6USiWir68v++mnnypH74dCoSieZdujvUg61MTERKZQKEBbWxtGc0yvs7NTy83NrX+i7WrS1dVVWllZiaurq4d7dJ6enoP37t37vaurCz00NISYmJjIeTzeND6fP6KB0N3djaFQKBPPwoEmBIPga0QgEJTP2vK0tbUdrKysxI+3nouLy6BSqQRnz57VUadDn4ZCoSiXLVv2cNmyZQ+Li4u7vL29p1VUVOA004oAAMDj8SRKpRIUFxeT1OnQ7u5uVFVVFSEiImJSs/60tLQm3dp2dXUdVKlUoKWlRSsgIGDMFjyPxxPn5+dTJRIJMlZv8MaNG9ppaWmNYWFhvQA8nmre0dExuamxz8DS0lJy6dIl7fXr1w/3BktLS7UtLS3FE603moODgwSLxarOnTtH5nK5HU9b/tdff9Wm0WhSzZRoRkbGE71cS0tL2YYNGzo3bNjQmZycTN+/fz9t9+7drQAAQCQSVWFhYb1hYWG9TU1N95lMJl8oFGprpuHVzp07V6XZ07h06RJp3bp1rLNnz1ba2tpKxjtOe3v7wcrKyhGp08uXL2t7e3v3rF69uhuAxxOC6urq8GQyecKxWmdn58H09HQtFAqlGi/V/7zbfpF0qLu7+yAGg1EVFhaSV65c2Q0AALW1tVp1dXV4Dw+PSQfBwcFBpK6uDu/p6fnEZ17985KysjJcRUUF6bPPPmvVfL6iooJgb28/qRm70NhgEHzDJScnt/7tb3/jrFu3ThoeHt6NwWDAb7/9Rrhy5QopKyurmcfjDb3//vsPV69ezerv72+cOXPmYENDA7a2tharviBo+uyzz4yMjY1lM2bMGMRisapDhw5RtbW1FWw2+4leJZfLHfrfhcX866+/FlGpVEViYiKDRCIpo6KiXmjq+1h4PN7QggULulesWMHq7OxsdnV1Hejs7MQIhUIdS0vLoaVLl/bEx8e3Hzp0yNDf399iy5Yt9/X19RWlpaUkLpcrnjVrlpjFYkm+++47fQ8Pj4H+/n5UQkKCqZaW1kuvHL1+/fq2ZcuWWaSnpw/4+/v3FRYW6hYUFFDz8vJqn2U7FApF+dFHHz3Ytm0bA4fDKX18fPrb29vRV69eJW3cuPGJoGhjYyNpb2/H7tu3j/ree+/1nzlzRregoGDE7MBly5aZ+fn59dra2kp6enrQQqGQzOFwJAA8nu0ql8sRDw+PATKZrMjLy6NiMBjVeAFtdMNIPSbF4/EkxsbG4/ZAfH19e1evXs2SyWRA/fMcS0tLSWFhIfXHH3/UptFo8t27d9NaWlqwZDJ5woZDYGBgn0Ag6P/rX//K2bZtWzOPx5M0NzdrnT17VnfevHl9vr6+/c+77RdJh+rr6ytCQ0M7U1JSTOl0uszQ0FAeHx9vZm1tLdacLMNms+2ioqLa1ePH0dHRpoGBgT2WlpbS1tZWrdTUVGOxWIyOjo4e/k4dOHCAYmhoKLe0tBy6efMmcePGjWY+Pj49oxsq165d005KShqzNw5NDvyJxBsuJCSk79ixYzUXL17U8fLymu7p6Tk9IyODzmQyh7+8J06cqA8KCupOSEhg8ng8+6ioKFZvb++YuU0dHR1FZmamkaen5/SZM2falpeXE0+ePFk9Og2rduTIkQYHB4eB0NBQjpeX13SJRIIqKiqqUk9QeNmOHTvWEBYW1rVlyxZTPp9vHxwcbFVSUqJjYWEhBeDx5AqhUHhPJpMh8+fPt3F1dbXNysqiqQNdTk5Ow8DAAMrd3d02IiLC4sMPP+xkMBjjThR6XkuWLOlJS0tr2rdvn5FAILD75ptvaDt27GjUnBQzWXv27GmJjY1t27Vrl4mDg4PdokWLOM3NzWOOYS5evLh3xYoVbUlJSWbOzs52Fy5cICcmJo7oHSiVSvDxxx8zBQKBvZ+fn7WhoaH822+/rQfgcfowLy/PwNvb20YgENgVFhZS8vLyajUnz7wMoaGhvTgcTpWfn6+rfiwtLe0+n88fCA4OtvL29rYhkUjKwMDAp05WQqFQQCgUVru7uz+Ki4tjcblc+4iICMvq6mq8mZmZ7EW2/aJycnKafH19H0ZGRlr6+PhMI5FIyqKiomoM5o/+RUNDA14zLdva2opdtmyZBZfLtQ8LC7PEYrGqX3755a61tbVUYxmtqKgoNp/Pt09ISDD74IMPugoKCuo09y0UCkmPHj1CL1269ImhEmjyEHWO/21x+/btBj6fP+5sOwiC3gw7d+40PHXqFOXy5ctVf/axvIvmz59vwefzB9UTzcZz+/ZtAz6fz3pNh/XWgelQCIJeifj4+I7u7m50V1cX+l2+ddqfQSwWI/b29uLNmzdP6uYF0PhgTxCCIOgdBnuCE4NjghAEQdCUBYMgBEEQNGW9jUFQqVQq4R0SIAiCnuJ/18rXXhXmbfLWBUEEQdrEYjEsHQJBEPQUYrEYjyDIlKx/OVlvXRCUy+WfNzQ0YAcGBgiwRwhBEPQkpVKJDAwMEBoaGrByufzzP/t43mRv3exQAAC4efPmPAwGk6JSqejgLQzkEARBr5gSQZA2uVz+uaOj449/9sG8yd7KIAhBEARBLwPsRUEQBEFTFgyCEARB0JQFgyAEQRA0ZcEgCEEQBE1ZMAhCEARBU9b/A1iygTfvn3wJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 504x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from itertools import cycle\n",
    "# setup plot details\n",
    "colors = cycle(['navy', 'turquoise', 'darkorange', 'cornflowerblue', 'teal'])\n",
    "\n",
    "n_classes=5\n",
    "plt.figure(figsize=(7, 8))\n",
    "f_scores = np.linspace(0.2, 0.8, num=4)\n",
    "lines = []\n",
    "labels = []\n",
    "for f_score in f_scores:\n",
    "    x = np.linspace(0.01, 1)\n",
    "    y = f_score * x / (2 * x - f_score)\n",
    "    l, = plt.plot(x[y >= 0], y[y >= 0], color='gray', alpha=0.2)\n",
    "    plt.annotate('f1={0:0.1f}'.format(f_score), xy=(0.9, y[45] + 0.02))\n",
    "\n",
    "lines.append(l)\n",
    "labels.append('iso-f1 curves')\n",
    "#l, = plt.plot(recall[\"micro\"], precision[\"micro\"], color='gold', lw=2)\n",
    "#lines.append(l)\n",
    "#labels.append('micro-average Precision-recall (area = {0:0.2f})'\n",
    "#              ''.format(average_precision[\"micro\"]))\n",
    "\n",
    "for i, color in zip(range(n_classes), colors):\n",
    "    l, = plt.plot(recall[i], precision[i], color=color, lw=2)\n",
    "    lines.append(l)\n",
    "    labels.append('Precision-recall for class {0} (area = {1:0.2f})'\n",
    "                  ''.format(i, average_precision[i]))\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.subplots_adjust(bottom=0.25)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall curve to multi-class')\n",
    "plt.legend(lines, labels, loc=(0, -.38), prop=dict(size=14))\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting original labels ice on/off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def iceonoff(labels_path):\n",
    "    dates = []\n",
    "    all_dates = []\n",
    "    dictionary = {}\n",
    "    per_day_analysis=[]\n",
    "    per_image_analysis=[]\n",
    "    files = (glob.glob(labels_path + \"/*\"))\n",
    "    files = (sorted(files))\n",
    "    for file in files:\n",
    "        im=Image.open(file)\n",
    "        match=re.search(r'\\d{4}_\\d{4}', file).group()\n",
    "    \n",
    "        water = frozen = 0\n",
    "        extra=0\n",
    "        pixels = list(im.getdata())\n",
    "        for i in pixels:\n",
    "            if i==1:\n",
    "                water+=1\n",
    "            elif i==2 or i==3 or i==4:\n",
    "                frozen+=1\n",
    "            else:\n",
    "                extra=1\n",
    "            \n",
    "        lake = water+frozen\n",
    "        coverage=round(float(frozen/lake),2)\n",
    "    \n",
    "        per_image_analysis.append(coverage)\n",
    "        all_dates.append(match)\n",
    "    \n",
    "        if match in dictionary.keys():\n",
    "            dictionary[match].append(coverage)\n",
    "        else:\n",
    "            dictionary[match]=[coverage]\n",
    "    \n",
    "    for key, values in dictionary.items():\n",
    "        dates.append(key)\n",
    "        per_day_analysis.append(np.median(values))\n",
    "        \n",
    "    return all_dates, dates, per_day_analysis, per_image_analysis\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels_path = \"labels_cam1_raw\"\n",
    "pred_path = \"cam1_predicted\"\n",
    "all_dates, dates, per_day_analysis, per_image_analysis = iceonoff(labels_path)\n",
    "all_dates, dates_pred, per_day_analysis_pred, per_image_analysis_pred = iceonoff(pred_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as plticker\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "fig = plt.figure(figsize=(16,2))\n",
    "ax = fig.add_subplot()\n",
    "#fig, ax = plt.subplots()\n",
    "ax.plot(dates[:-30], per_day_analysis[:-30], label=\"Ground Truth\" )\n",
    "ax.plot(dates[:-30], per_day_analysis_pred[:-30], label=\"per day prediction\")\n",
    "loc = plticker.MultipleLocator(base=3.0) # this locator puts ticks at regular intervals\n",
    "ax.xaxis.set_major_locator(loc)\n",
    "ax.legend(loc='best')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"Frozen area\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.savefig('cam1per_day.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as plticker\n",
    "from scipy.ndimage.filters import median_filter\n",
    "\n",
    "fig = plt.figure(figsize=(16,2))\n",
    "ax = fig.add_subplot()\n",
    "#fig, ax = plt.subplots()\n",
    "ysmoothed=median_filter(per_image_analysis_pred[:-30], size=5)\n",
    "\n",
    "ax.plot(all_dates[:-30], per_image_analysis[:-30], label=\"Ground Truth\" )\n",
    "ax.plot(all_dates[:-30], ysmoothed, label=\"per image prediction\", markevery=100)\n",
    "loc = plticker.MultipleLocator(base=3.0) # this locator puts ticks at regular intervals\n",
    "ax.xaxis.set_major_locator(loc)\n",
    "ax.legend(loc='best')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"Frozen area\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "plt.savefig('cam1.png')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying median filter "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Params\n",
    "temporal=5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def median_filter(logits_path):\n",
    "    logit_files = (glob.glob(logits_path + \"/*\"))\n",
    "    logit_files = (sorted(logit_files))\n",
    "    #logits=np.array([np.array(np.load(file)) for file in logit_files])\n",
    "    dates = []\n",
    "    all_dates = []\n",
    "    dictionary = {}\n",
    "    per_day_analysis_with_median=[]\n",
    "    per_image_analysis_with_median=[]\n",
    "    per_image_analysis_with_median = []\n",
    "    \n",
    "    i = 3\n",
    "    \n",
    "    for file in logit_files:\n",
    "        match=re.search(r'\\d{4}_\\d{4}', file).group()\n",
    "        #print(match)\n",
    "        \n",
    "        imgs = np.array([np.array(np.load(file)) for file in logit_files[i-3:i+4]])\n",
    "        #print(imgs.shape)  \n",
    "        \n",
    "        i=i+1\n",
    "        \n",
    "    #for i in range(5, len(logit_files)-temporal):\n",
    "        \n",
    "        #pre_pixels= np.argmax(logits[i], axis=1)\n",
    "        #imgs = logits[i-5:i+6]\n",
    "        \n",
    "        med = np.median(imgs, axis=0) \n",
    "        post_pixels = np.argmax(med, axis=1)\n",
    "        \n",
    "        water=frozen=extra=0\n",
    "        \n",
    "        for j in post_pixels:\n",
    "            if j==1:\n",
    "                water+=1\n",
    "            elif j==2 or j==3 or j==4:\n",
    "                frozen+=1\n",
    "            else:\n",
    "                extra=1\n",
    "            \n",
    "        lake = water+frozen\n",
    "        coverage=round(float(frozen/lake),2)\n",
    "        #print(coverage)\n",
    "        per_image_analysis_with_median.append(coverage)\n",
    "        all_dates.append(match)\n",
    "    \n",
    "        if match in dictionary.keys():\n",
    "            dictionary[match].append(coverage)\n",
    "        else:\n",
    "            dictionary[match]=[coverage]\n",
    "    \n",
    "    for key, values in dictionary.items():\n",
    "        dates.append(key)\n",
    "        per_day_analysis_with_median.append(np.median(values))\n",
    "        \n",
    "        \n",
    "        \n",
    "    return dates, per_day_analysis_with_median"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "logits_path = \"logits\"\n",
    "#print(logits.shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates, per_day_analysis_with_median = median_filter(logits_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "130\n"
     ]
    }
   ],
   "source": [
    "print(len(per_day_analysis_with_median))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Post median filter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as plticker\n",
    "from scipy.ndimage.filters import median_filter\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "\n",
    "fig = plt.figure(figsize=(16,2))\n",
    "ax = fig.add_subplot()\n",
    "#fig, ax = plt.subplots()\n",
    "ysmoothed_median=median_filter(per_day_analysis_with_median[:-31], size=3)\n",
    "\n",
    "ax.plot(dates[:-31], per_day_analysis[:-31], label=\"Ground Truth\", color=\"green\")\n",
    "ax.plot(dates[:-31], per_day_analysis_pred[:-31], label=\"prediction pre median filter\", color=\"blue\")\n",
    "ax.plot(dates[:-31], ysmoothed_median, label=\"prediction post median filter\",color=\"red\")\n",
    "loc = plticker.MultipleLocator(base=3.0) # this locator puts ticks at regular intervals\n",
    "ax.xaxis.set_major_locator(loc)\n",
    "ax.legend(loc='best')\n",
    "plt.xticks(rotation=45)\n",
    "plt.ylabel(\"Frozen area\")\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with temporal=2: Improvement on Moritz nonPTZ Cam 1 16-17\n",
    "##### 0.75\n",
    "##### 0.79"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### with temporal=3: Improvement on Moritz nonPTZ Cam 1 16-17\n",
    "##### 0.75\n",
    "##### 0.80"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(819, 393250, 5)\n"
     ]
    }
   ],
   "source": [
    "with tf.Session() as sess:\n",
    "    with tf.device('/cpu:0'):\n",
    "        print(logits.shape)\n",
    "        pre_pixels= np.argmax(logits, axis=2)\n",
    "        ytrueT = tf.convert_to_tensor(labels, np.float32)\n",
    "        ypredT = tf.convert_to_tensor(pre_pixels, np.float32)\n",
    "        iou,conf_mat = tf.metrics.mean_iou(ytrueT, ypredT, num_classes=5)\n",
    "        sess.run(tf.local_variables_initializer())\n",
    "        sess.run([conf_mat])\n",
    "        miou = sess.run([iou])\n",
    "        print(miou)\n",
    "#     for i in range(2, len(files)-temporal):\n",
    "        \n",
    "#         pre_pixels= np.argmax(logits[i], axis=1)\n",
    "#         imgs = logits[i-2:i+3]\n",
    "#         med = np.median(imgs, axis=0)        \n",
    "#         post_pixels = np.argmax(med, axis=1)\n",
    "#         ytrueT = tf.convert_to_tensor(labels[i], np.float32)\n",
    "#         ypredT = tf.convert_to_tensor(pre_pixels, np.float32)\n",
    "#         iou,conf_mat = tf.metrics.mean_iou(ytrueT, ypredT, num_classes=3)\n",
    "#         sess.run(tf.local_variables_initializer())\n",
    "#         sess.run([conf_mat])\n",
    "#         miou = sess.run([iou])\n",
    "#     print(miou)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
